FKNMS Stephanocoenia intersepta Population Genetics and Algal Symbiont Communities
Ryan Eckert
version: 19 March, 2024
About this document
All analyses preformed with R version 4.2.2.
Basic setup of R environment
Loading required packages
For the following analyses we will require the use of a number of different R packages. We can use the following code to quickly load in the packages and install any packages not previously installed in the R console.
if (!require("pacman")) install.packages("pacman")
pacman::p_load_gh("pmartinezarbizu/pairwiseAdonis/pairwiseAdonis", "ropensci/rnaturalearthhires", "KarstensLab/microshades")
pacman::p_load("cowplot", "car", "ggrepel", "ggspatial", "paletteer", "patchwork", "rgdal", "rnaturalearth", "sf", "Hmisc", "MCMC.OTU", "pairwiseAdonis", "RColorBrewer", "Redmonder", "flextable", "lubridate", "officer", "adegenet", "dendextend", "gdata", "ggdendro", "hierfstat", "kableExtra", "poppr", "reshape2", "StAMPP", "vcfR", "vegan", "boa", "magick", "rgeos", "sdmpredictors", "ggcorrplot", "tidyverse", "TeachingDemos", "LaplacesDemon", "adespatial", "ggnewscale", "ggbeeswarm", "multcomp", "rstatix", "R.utils", "graph4lg")
options("scipen" = 10)
# load("fknmsSint.RData")Making color palettes to use throughout all plots
# flPal = c(paletteer_d("vapoRwave::jazzCup")[c(1, 3:4)], "#4B31B3")
# flPal = c("#D72000", "#FFAD0A", "#1BB6AF", "#132157")
# flPal = c("#E73F74", "#F2B701", "#3969AC", "#7F3C8D")
flPal = paletteer_c("viridis::turbo", n = 9, direction = -1)[c(6:9)]
boundPal = c("gray30", paletteer_d("vapoRwave::vapoRwave")[10])
pink = "#FF6A8BFF"
purple = paletteer_d("vapoRwave::vapoRwave")[10]
kColPal = c(paletteer_d("rcartocolor::BluYl")[c(7, 5, 3)], "#f5e97a", "azure3")
profPal = rev(c(microshades_palette("micro_green", 5), microshades_palette("micro_cvd_turquoise", 5), microshades_palette("micro_cvd_orange", 3),microshades_palette("micro_cvd_purple", 1, lightest = F), microshades_palette("micro_purple", 5)))
colPalZoox = c("#807dba", "#F09163", "#48C9B0", "#FEEDA0")Sampling info
Map of study sites
fknmsSites = read.csv("../data/stephanocoeniaMetaData.csv", header = TRUE)
fknmsSites$depthZone = factor(fknmsSites$depthZone)
fknmsSites$depthZone = factor(fknmsSites$depthZone, levels = levels(fknmsSites$depthZone)[c(2,1)])
fknmsSites$site = factor(fknmsSites$site)
fknmsSites$site = factor(fknmsSites$site, levels = levels(fknmsSites$site)[c(4, 1, 3, 2)])
fknmsSites$date = mdy(fknmsSites$date) %>% format("%d %b %Y")
fknmsPops = fknmsSites %>% group_by(site) %>% summarise(latDD = mean(latDD), longDD = mean(longDD), n = n()) %>% droplevels()
fknmsSampleSites = fknmsSites %>% group_by(site, siteID, depthZone) %>% summarise(latDD = min(latDD), longDD = min(longDD))## `summarise()` has grouped output by 'site', 'siteID'. You can override using the
## `.groups` argument.fknmsBounds = read.csv("../data/shp/fknmsSPA.csv", header = TRUE)
states = st_as_sf(ne_states(country = c("United States of America")), scale = "count", crs = 4326) %>% filter(name_en %in% c("Florida", "Georgia", "Alabama"))
countries = st_as_sf(ne_countries(country = c("Cuba", "Mexico", "The Bahamas", "Bermuda"), scale = "Large"), crs = 4326)
bahamas = read_sf("../data/shp/bahamasShoreline.shp") %>% st_transform(crs = 4326)
cuba = read_sf("../data/shp/cubaShoreline.shp") %>% st_transform(crs = 4326)
florida = read_sf("../data/shp/floridaShoreline.shp") %>% st_transform(crs = 4326)
bathy = read_sf("../data/shp/flBathy.shp") %>% st_transform(crs = 4326) %>% subset(subset = DATASET %in% c("fl_shelf", "fl_coast"))
tortugasBathy = read_sf("../data/shp/tortugasBathy.shp") %>% st_transform(crs = 4326)Next we build a hi-res polygon of FL with the study site marked and a
zoomed in map of the colony locations. We use ggspatial to
add a north arrow and scale bar to the main map.
floridaMap = ggplot() +
geom_polygon(data = fknmsBounds[fknmsBounds$type == "Sanctuary",], aes(x = long, y = lat, group = location), alpha = 0.1, fill = "black", color = "black") +
geom_polygon(data = fknmsBounds[fknmsBounds$location == "FKNMS2",], aes(x = long, y = lat), fill = "aliceblue", color = NA) +
# geom_polygon(data = fknmsBounds, aes(x = long, y = lat, color = type, group = location), fill = NA, linewidth = 0) +
scale_fill_manual(values = flPal, name = "Site") +
scale_color_manual(values = boundPal, name = "Boundaries", labels = c("FKNMS", "SPA")) +
geom_point(data = fknmsSites, aes(x = longDD, y = latDD, shape = depthZone), color = NA, fill = NA) +
scale_shape_manual(values = c(21, 23), name = "Depth") +
geom_sf(data = florida, fill = "white", linewidth = 0.15) +
geom_sf(data = cuba, fill = "white", linewidth = 0.15) +
geom_sf(data = bahamas, fill = "white", linewidth = 0.15) +
geom_segment(aes(x = -80.1, y = 25.3, xend = -78.825, yend = 24.44), linewidth = 0.25) +
geom_segment(aes(x = -80.4, y = 25, xend = -80.27, yend = 23), linewidth = 0.25) +
geom_segment(aes(x = -81.75, y = 24.7, xend = -82.22, yend = 24.28), linewidth = 0.25) +
geom_segment(aes(x = -81.45, y = 24.7, xend = -80.78, yend = 24.28), linewidth = 0.25) +
geom_segment(aes(x = -83.25, y = 24.75, xend = -84.183, yend = 24.28), linewidth = 0.25) +
geom_segment(aes(x = -82.95, y = 24.75, xend = -82.74, yend = 24.28), linewidth = 0.25) +
geom_rect(aes(xmin = -80.4, xmax = -80.1, ymin = 25, ymax = 25.3), fill = NA, color = "black", linewidth = 0.25, alpha = 0.5) +
geom_rect(aes(xmin = -81.75, xmax = -81.45, ymin = 24.4, ymax = 24.7), fill = NA, color = "black", linewidth = 0.25, alpha = 0.5) +
geom_rect(aes(xmin = -83.25, xmax = -82.95, ymin = 24.45, ymax = 24.75), fill = NA, color = "black", linewidth = 0.25, alpha = 0.5) +
geom_point(data = fknmsPops, aes(x = longDD, y = latDD, fill = site), shape = 22, size = 2) +
coord_sf(xlim = c(-84, -79), ylim = c(23, 27)) +
scale_x_continuous(breaks = c(seq(-84, -79, by = 1))) +
scale_y_continuous(breaks = c(seq(23, 27, by = 1))) +
annotation_scale(location = "br", pad_x = unit(1.35, "cm"), text_pad = unit(-4, "cm")) +
guides(fill = guide_legend(override.aes = list(shape = 22, color = "black", size = 2, stroke = 0.25), order = 1), shape = guide_legend(override.aes = list(size = c(2.25, 2), stroke = 0.25, color = "black"), order = 2), color = "none") +
theme_bw() +
theme(panel.background = element_rect(fill = "aliceblue"),
plot.background = element_blank(),
panel.border = element_rect(color = "black", size = 1, fill = NA),
axis.title = element_blank(),
axis.ticks = element_line(color = "black"),
axis.text = element_text(color = "black"),
legend.position = c(0.905, 0.875),
legend.box.background = element_rect(linewidth = 0.35, fill = "white"),
legend.title = element_text(color = "black", size = 8),
legend.text = element_text(color = "black", size = 8),
legend.spacing = unit(-5, "pt"),
legend.key.size = unit(5, "pt"),
legend.background = element_blank()
)
floridaMap
largeMap = inset = ggplot() +
geom_sf(data = states, fill = "white", linewidth = 0.3) +
geom_sf(data = countries, fill = "white", linewidth = 0.3) +
geom_rect(aes(xmin = -84, xmax = -79, ymin = 23, ymax = 27), color = "black", fill = NA, alpha = 0.25, linewidth = 0.5) +
geom_rect(aes(xmin = -78.8, xmax = -77, ymin = 22.2, ymax = 22.6), fill = "aliceblue", color = NA) +
annotation_scale(location = "bl", pad_x = unit(2.25, "cm")) +
annotation_north_arrow(location = "tr", style = north_arrow_minimal(), pad_x = unit(-0.3, "cm")) +
coord_sf(xlim = c(-87, -76), ylim = c(22, 31)) +
theme_bw() +
theme(legend.title = element_text(size = 9, face = "bold"),
axis.ticks = element_blank(),
axis.text = element_blank(),
axis.title = element_blank(),
panel.background = element_rect(fill = "aliceblue"),
panel.border = element_rect(color = "black", size = 1, fill = NA),
legend.position = "none",
plot.background = element_blank())
# largeMap
inset = ggplot() +
geom_polygon(data = fknmsBounds[fknmsBounds$type == "Sanctuary",], aes(x = long, y = lat, group = location), alpha = 0.1, fill = "black", color = NA) +
geom_polygon(data = fknmsBounds[fknmsBounds$location == "FKNMS2",], aes(x = long, y = lat), fill = "aliceblue", color = NA) +
geom_segment(aes(x = -82.9645, xend = -82.4, y = 24.6, yend = 24.6), color = "gray92", size = .55) +
geom_sf(data = bathy, color = "gray75", size = 0.25) +
geom_polygon(data = fknmsBounds, aes(x = long, y = lat, color = type, group = location), fill = NA) +
scale_fill_manual(values = flPal, name = "Site") +
scale_color_manual(values = boundPal, name = "Boundaries", labels = c("FKNMS", "SPA")) +
geom_point(data = fknmsSampleSites, aes(x = longDD, y = latDD, fill = site, shape = depthZone, size = depthZone)) +
geom_sf(data = florida, fill = "white", size = 0.15) +
scale_shape_manual(values = c(21, 23), name = "Depth") +
scale_size_manual(values = c(1.625, 1.5)) +
theme_bw() +
theme(legend.title = element_text(size = 9, face = "bold"),
axis.ticks = element_blank(),
axis.text = element_blank(),
axis.title = element_blank(),
panel.background = element_rect(fill = "aliceblue"),
panel.border = element_rect(color = "black", size = 1, fill = NA),
legend.position = "none",
plot.background = element_blank())
# inset
upperKeys = inset +
annotation_scale(location = "bl", pad_x = unit(1.9, "cm")) +
coord_sf(xlim = c(-80.4, -80.1), ylim = c(25.0, 25.3)) +
scale_x_continuous(breaks = c(seq(-80.4, -80.0, by = .1))) +
scale_y_continuous(breaks = c(seq(25.0, 25.3, by = .1)))
lowerKeys = inset +
annotation_scale(location = "bl", pad_x = unit(1.9, "cm")) +
coord_sf(xlim = c(-81.75, -81.45), ylim = c(24.4, 24.7)) +
scale_x_continuous(breaks = c(seq(-81.7, -81.3, by = .1))) +
scale_y_continuous(breaks = c(seq(24.4, 24.7, by = .1)))
dryTortugas = ggplot() +
geom_polygon(data = fknmsBounds[fknmsBounds$type == "Sanctuary",], aes(x = long, y = lat, group = location), alpha = 0.1, fill = "black", color = NA) +
geom_polygon(data = fknmsBounds[fknmsBounds$location == "FKNMS2",], aes(x = long, y = lat), fill = "aliceblue", color = NA) +
geom_segment(aes(x = -82.9645, xend = -82.4, y = 24.6, yend = 24.6), color = "gray92", size = .55) +
geom_sf(data = tortugasBathy, color = "gray75", size = 0.25) +
geom_polygon(data = fknmsBounds, aes(x = long, y = lat, color = type, group = location), fill = NA) +
scale_fill_manual(values = flPal, name = "Site") +
scale_color_manual(values = boundPal, name = "Boundaries", labels = c("FKNMS", "SPA")) +
geom_point(data = fknmsSites, aes(x = longDD, y = latDD, fill = site, shape = depthZone, size = depthZone)) +
geom_sf(data = florida, fill = "white", size = 0.25) +
scale_shape_manual(values = c(21, 23), name = "Depth") +
scale_size_manual(values = c(1.625, 1.5)) +
annotation_scale(location = "bl", pad_x = unit(1.9, "cm")) +
coord_sf(xlim = c(-83.25, -82.95), ylim = c(24.45, 24.75)) +
scale_x_continuous(breaks = c(seq(-83.2, -82.9, by = .1))) +
scale_y_continuous(breaks = c(seq(24.4, 24.7, by = .1))) +
theme_bw() +
theme(legend.title = element_text(size = 9, face = "bold"),
axis.ticks = element_blank(),
axis.text = element_blank(),
axis.title = element_blank(),
panel.background = element_rect(fill = "aliceblue"),
panel.border = element_rect(color = "black", size = 1, fill = NA),
legend.position = "none",
plot.background = element_blank())popData = read.csv("../data/stephanocoeniaMetaData.csv")[-c(66, 68, 164, 166, 209, 211),] %>% dplyr::select("sample" = tubeID, "pop" = site, "depth" = depthZone, "depthm" = depthM)
popData$popdepth = as.factor(paste(popData$pop, popData$depth, sep = ""))
popData$popdepth = factor(popData$popdepth, levels(popData$popdepth)[c(4, 3, 6, 5, 2, 1, 8, 7)])
pcadmix = read.table("../data/snps/k/Clumpp3xK4.output") %>%dplyr::select(V6, V7, V8, V9)
pcadmix %>% summarise(sum(V6),sum(V7), sum(V8), sum(V9)) ## sum(V6) sum(V7) sum(V8) sum(V9)
## 1 128.7501 43.4095 31.2726 16.5678pcadmix = popData %>% cbind(pcadmix) %>% rename("cluster1" = "V6", "cluster2" = "V7", "cluster3" = "V8", "cluster4" = "V9") %>%dplyr::select(order(colnames(.)))
pcadmixClust = pcadmix %>% mutate(cluster = ifelse(cluster1 < 0.75 & cluster2 < 0.75 & cluster3 < 0.75 & cluster4 < 0.75, "NA", ifelse(cluster1 >=0.75, 1, ifelse(cluster2 >= 0.75, 2, ifelse(cluster3 >= 0.75, 3,ifelse(cluster4 >= 0.75, 4, 0))))))
pcadmix = pcadmix %>% mutate(pcadmixClust)
pcadmix$cluster = as.factor(pcadmix$cluster)
levels(pcadmix$cluster) = c("Blue", "Teal", "Green", "Yellow", "Admixed")
siteLineages = pcadmix %>% dplyr::select(popdepth, cluster) %>%
group_by(popdepth) %>% count(cluster) %>% mutate(Freq = n/sum(n)) %>% apply(2, function(x) gsub("\\s+", "", x)) %>% as.data.frame()
pieCol = c("Blue" = kColPal[1], Teal = kColPal[2], "Green" = kColPal[3], "Yellow" = kColPal[4], "Admixed" = kColPal[5])
pieDf = siteLineages %>% group_by(popdepth) %>% mutate("ymax" = cumsum(Freq)) %>% mutate("ymin" = c(0, head(ymax, n=-1)))
ukMeso = ggplot() +
geom_rect(aes(ymax=1, ymin = 0, xmax = 4, xmin = 2), fill = flPal[1], alpha = 1) +
geom_rect(data = pieDf%>% filter(popdepth == "UpperKeysMesophotic"), aes(ymax=ymax, ymin = ymin, xmax = 4, xmin = 3, fill = cluster), color = "black", size = 0.25) +
annotate(geom = "text", x = 2, y = 0.75, label = "43.9", size = 2.5, fontface = "bold", color = "black") +
scale_fill_manual(values = pieCol)+
coord_polar(theta="y") +
xlim(c(2, 4)) +
theme_void() +
theme(legend.position = "none", panel.background = element_blank())
lkMeso = ggplot() +
geom_rect(aes(ymax=1, ymin = 0, xmax = 4, xmin = 2), fill = flPal[2], alpha = 1) +
geom_rect(data = pieDf%>% filter(popdepth == "LowerKeysMesophotic"), aes(ymax=ymax, ymin = ymin, xmax = 4, xmin = 3, fill = cluster), color = "black", size = 0.25) +
annotate(geom = "text", x = 2, y = 0.75, label = "32.8", size = 2.5, fontface = "bold") +
scale_fill_manual(values = pieCol)+
coord_polar(theta="y") +
xlim(c(2, 4)) +
theme_void() +
theme(legend.position = "none", panel.background = element_blank())
tbMeso = ggplot() +
geom_rect(aes(ymax=1, ymin = 0, xmax = 4, xmin = 2), fill = flPal[3], alpha = 1) +
geom_rect(data = pieDf%>% filter(popdepth == "TortugasBankMesophotic"), aes(ymax=ymax, ymin = ymin, xmax = 4, xmin = 3, fill = cluster), color = "black", size = 0.25) +
annotate(geom = "text", x = 2, y = 0.75, label = "32.0", size = 2.5, fontface = "bold", color = "white") +
scale_fill_manual(values = pieCol)+
coord_polar(theta="y") +
xlim(c(2, 4)) +
theme_void() +
theme(legend.position = "none", panel.background = element_blank())
rhMeso = ggplot() +
geom_rect(aes(ymax=1, ymin = 0, xmax = 4, xmin = 2), fill = flPal[4], alpha = 1) +
geom_rect(data = pieDf %>% filter(popdepth == "Riley'sHumpMesophotic"), aes(ymax=ymax, ymin = ymin, xmax = 4, xmin = 3, fill = cluster), color = "black", size = 0.25) +
annotate(geom = "text", x = 2, y = 0.75, label = "33.2", size = 2.5, fontface = "bold", color = "white") +
scale_fill_manual(values = pieCol)+
coord_polar(theta="y") +
xlim(c(2, 4)) +
theme_void() +
theme(legend.position = "none", panel.background = element_blank())
ukShal = ggplot() +
geom_rect(aes(ymax=1, ymin = 0, xmax = 4, xmin = 2), fill = flPal[1]) +
geom_rect(data = pieDf%>% filter(popdepth == "UpperKeysShallow"), aes(ymax=ymax, ymin = ymin, xmax = 4, xmin = 3, fill = cluster), color = "black", size = 0.25) +
annotate(geom = "text", x = 2, y = 0.75, label = "23.6", size = 2.5, fontface = "bold", color = "black") +
scale_fill_manual(values = pieCol)+
coord_polar(theta="y") +
xlim(c(2, 4)) +
theme_void() +
theme(legend.position = "none", panel.background = element_blank())
lkShal = ggplot() +
geom_rect(aes(ymax=1, ymin = 0, xmax = 4, xmin = 2), fill = flPal[2]) +
geom_rect(data = pieDf%>% filter(popdepth == "LowerKeysShallow"), aes(ymax=ymax, ymin = ymin, xmax = 4, xmin = 3, fill = cluster), color = "black", , size = 0.25) +
annotate(geom = "text", x = 2, y = 0.75, label = "18.0", size = 2.5, fontface = "bold") +
scale_fill_manual(values = pieCol)+
coord_polar(theta="y") +
xlim(c(2, 4)) +
theme_void() +
theme(legend.position = "none", panel.background = element_blank())
tbShal = ggplot() +
geom_rect(aes(ymax=1, ymin = 0, xmax = 4, xmin = 2), fill = flPal[3]) +
geom_rect(data = pieDf%>% filter(popdepth == "TortugasBankShallow"), aes(ymax=ymax, ymin = ymin, xmax = 4, xmin = 3, fill = cluster), color = "black", size = 0.25) +
annotate(geom = "text", x = 2, y = 0.75, label = "21.1", size = 2.5, fontface = "bold", color = "white") +
scale_fill_manual(values = pieCol)+
coord_polar(theta="y") +
xlim(c(2, 4)) +
theme_void() +
theme(legend.position = "none", panel.background = element_blank())
rhShal = ggplot() +
geom_rect(aes(ymax=1, ymin = 0, xmax = 4, xmin = 2), fill = flPal[4]) +
geom_rect(data = pieDf %>% filter(popdepth == "Riley'sHumpShallow"), aes(ymax=ymax, ymin = ymin, xmax = 4, xmin = 3, fill = cluster), color = "black", size = 0.25) +
annotate(geom = "text", x = 2, y = 0.75, label = "26.4", size = 2.5, fontface = "bold", color = "white") +
scale_fill_manual(values = pieCol)+
coord_polar(theta = "y") +
xlim(c(2, 4)) +
theme_void() +
theme(legend.position = "none", panel.background = element_blank())map = (floridaMap +
inset_element(largeMap, top = 1.01, right = 0.33, bottom = 0.63, left = -0.005, ignore_tag = TRUE) +
inset_element(dryTortugas, top = 0.36, right = 0.2875, bottom = -0.01, left = -0.0075, ignore_tag = TRUE) +
inset_element(lowerKeys, top = 0.36, right = 0.645, bottom = -0.01, left = 0.35, ignore_tag = TRUE) +
inset_element(upperKeys, top = 0.395, right = 1.00, bottom = 0.025, left = 0.705, ignore_tag = TRUE) +
inset_element(ukShal, top = 0.374, right = 0.99, bottom = 0.274, left = 0.89, ignore_tag = TRUE) +
inset_element(ukMeso, top = 0.284, right = 0.99, bottom = 0.184, left = 0.89, ignore_tag = TRUE) +
inset_element(lkShal, top = 0.209, right = 0.466, bottom = 0.109, left = 0.366, ignore_tag = TRUE) +
inset_element(lkMeso, top = 0.119, right = 0.466, bottom = 0.019, left = 0.366, ignore_tag = TRUE) +
inset_element(rhShal, top = 0.209, right = 0.11, bottom = 0.109, left = 0.01, ignore_tag = TRUE) +
inset_element(rhMeso, top = 0.119, right = 0.11, bottom = 0.019, left = 0.01, ignore_tag = TRUE) +
inset_element(tbShal, top = 0.338, right = 0.278, bottom = 0.238, left = 0.178, ignore_tag = TRUE) +
inset_element(tbMeso, top = 0.248, right = 0.278, bottom = 0.148, left = 0.178, ignore_tag = TRUE)
)
ggsave("../figures/figure1.png", plot = map, height = 7, width = 7, units = "in", dpi = 300)
ggsave("../figures/figure1.svg", plot = map, height = 7, width = 7, units = "in", dpi = 300)
S. intersepta population genetics from SNPs
Analyzing 2bRAD generated SNPs (24,670 loci) for population structure//genetic connectivity across sites and depth zones in FKNMS
How many reads?
rawSintReads = read.delim("../data/snps/sintRawReadCounts", header = FALSE)
colnames(rawSintReads) = c("sample", "reads")
head(rawSintReads)## sample reads
## 1 FKSi1-1 42167284
## 2 FKSi1-2 54651139
## 3 FKSi1-3 41635251
## 4 FKSi1-4 37754282
## 5 FKSi1-5 39973126
## 6 FKSi1-6 45580831#total reads
sum(rawSintReads$reads)## [1] 796139328#average reads/sample
(sum(rawSintReads$reads)/226)## [1] 3522740Identifiying clonal multi-locus genotypes
Dendrogram with clones
Identification of any natural clones using technical replicates as a baseline for clonality between samples.
cloneBams = read.csv("../data/stephanocoeniaMetaData.csv") # list of bam files
# cloneMa = as.matrix(read.table("../data/snps/clones/sintClones.ibsMat")) # reads in IBS matrix produced by ANGSD
cloneMa = as.matrix(read.table("~/Desktop/angsd3x/sintClones3x.ibsMat")) # reads in IBS matrix produced by ANGSD
dimnames(cloneMa) = list(cloneBams[,1],cloneBams[,1])
clonePops = cloneBams$site
cloneDepth = cloneBams$depthZone
cloneDend = cloneMa %>% as.dist() %>% hclust(.,"ave") %>% as.dendrogram()
cloneDData = cloneDend %>% dendro_data()
# Making the branches hang shorter so we can easily see clonal groups
cloneDData$segments$yend2 = cloneDData$segments$yend
for(i in 1:nrow(cloneDData$segments)) {
if (cloneDData$segments$yend2[i] == 0) {
cloneDData$segments$yend2[i] = (cloneDData$segments$y[i] - 0.01)}}
cloneDendPoints = cloneDData$labels
cloneDendPoints$pop = clonePops[order.dendrogram(cloneDend)]
cloneDendPoints$depth=cloneDepth[order.dendrogram(cloneDend)]
rownames(cloneDendPoints) = cloneDendPoints$label
# Making points at the leaves to place symbols for populations
point = as.vector(NA)
for(i in 1:nrow(cloneDData$segments)) {
if (cloneDData$segments$yend[i] == 0) {
point[i] = cloneDData$segments$y[i] - 0.01
} else {
point[i] = NA}}
cloneDendPoints$y = point[!is.na(point)]
techReps = c("SFK066.1", "SFK066.2", "SFK066.3", "SFK162.1", "SFK162.2", "SFK162.3", "SFK205.1", "SFK205.2", "SFK205.3")
cloneDendPoints$depth = factor(cloneDendPoints$depth)
cloneDendPoints$depth = factor(cloneDendPoints$depth, levels(cloneDendPoints$depth)[c(2,1)])
cloneDendPoints$pop = factor(cloneDendPoints$pop)
cloneDendPoints$pop = factor(cloneDendPoints$pop,levels(cloneDendPoints$pop)[c(4, 1, 3, 2)])
cloneDendA = ggplot() +
geom_rect(aes(xmin = 47.25, xmax = 50.75, ymin = 0.03, ymax = 0.085), fill = pink, alpha = 0.4) +
geom_rect(aes(xmin = 164.25, xmax = 167.75, ymin = 0.065, ymax = 0.12), fill = pink, alpha = 0.4) +
geom_rect(aes(xmin = 219.25, xmax = 222.75, ymin = 0.065, ymax = 0.12), fill = pink, alpha = 0.4) +
geom_segment(data = segment(cloneDData), aes(x = x, y = y, xend = xend, yend = yend2), size = 0.5) +
geom_point(data = cloneDendPoints, aes(x = x, y = y, fill = pop, shape = depth), size = 4, stroke = 0.25) +
scale_fill_manual(values = flPal, name= "Site:") +
scale_shape_manual(values = c(21, 23), name = "Depth Zone:") +
geom_hline(yintercept = 0.12, color = pink, lty = 5, size = 1) + # creating a dashed line to indicate a clonal distance threshold
geom_text(data = subset(cloneDendPoints, subset = label %in% techReps), aes(x = x, y = (y - .02), label = label), angle = 90) + # spacing technical replicates further from leaf
geom_text(data = subset(cloneDendPoints, subset = !label %in% techReps), aes(x = x, y = (y - .015), label = label), angle = 90) +
labs(y = "Genetic distance (1 - IBS)") +
guides(fill = guide_legend(override.aes = list(shape = 22, size = 10), ncol = 2), shape = guide_legend(override.aes = list(size = 8), ncol = 1, order = 1)) +
coord_cartesian(xlim = c(5, 218), ylim = c(0.03, 0.25)) +
theme_classic()
cloneDend = cloneDendA + theme(
axis.title.x = element_blank(),
axis.text.x = element_blank(),
axis.line.x = element_blank(),
axis.ticks.x = element_blank(),
axis.title.y = element_text(size = 24, color = "black", angle = 90),
axis.text.y = element_text(size = 20, color = "black"),
axis.line.y = element_line(),
axis.ticks.y = element_line(),
panel.grid = element_blank(),
panel.border = element_blank(),
panel.background = element_blank(),
legend.key = element_blank(),
legend.title = element_text(size = 24),
legend.text = element_text(size = 20),
legend.position = "bottom")
# cloneDend
ggsave("../figures/rmd/cloneDend3x.png", plot = cloneDend, height = 8, width = 35, units = "in", dpi = 300)
We removed the technical replicates/clones and re-ran ANGSD for all the following pop-gen analyses.
Dendrogram without clones
bams = read.csv("../data/stephanocoeniaMetaData.csv")[-c(66,68,164,166,209,211),] # list of bams files and their populations (tech reps removed)
ma = as.matrix(read.table("../data/snps/sintNoClones3x.ibsMat")) # reads in IBS matrix produced by ANGSD
# ma = as.matrix(read.table("../data/snps/sintNoClones.ibsMat")) # reads in IBS matrix produced by ANGSD
dimnames(ma) = list(bams[,1],bams[,1])
pops = bams$site
depth = bams$depthZone
dend = ma %>% as.dist() %>% hclust(.,"ave") %>% as.dendrogram()
dData = dend %>% dendro_data()
# Making the branches hang shorter so we can easily see clonal groups
dData$segments$yend2 = dData$segments$yend
for(i in 1:nrow(dData$segments)) {
if (dData$segments$yend2[i] == 0) {
dData$segments$yend2[i] = (dData$segments$y[i] - 0.01)}}
dendPoints = dData$labels
dendPoints$pop = pops[order.dendrogram(dend)]
dendPoints$depth = depth[order.dendrogram(dend)]
rownames(dendPoints) = dendPoints$label
# Making points at the leaves to place symbols for populations
point = as.vector(NA)
for(i in 1:nrow(dData$segments)) {
if (dData$segments$yend[i] == 0) {
point[i] = dData$segments$y[i] - 0.01
} else {
point[i] = NA}}
dendPoints$y = point[!is.na(point)]
dendPoints$depth = factor(dendPoints$depth)
dendPoints$depth = factor(dendPoints$depth, levels(dendPoints$depth)[c(2,1)])
dendPoints$pop = factor(dendPoints$pop)
dendPoints$pop = factor(dendPoints$pop, levels(dendPoints$pop)[c(4, 1, 3, 2)])
dendNoCloneA = ggplot() +
geom_segment(data = segment(dData), aes(x = x, y = y, xend = xend, yend = yend2), size = 0.5) +
geom_point(data = dendPoints, aes(x = x, y = y, fill = pop, shape = depth), size = 4, stroke = 0.25) +
scale_fill_manual(values = flPal, name= "Site:")+
scale_shape_manual(values = c(21, 23), name = "Depth Zone:")+
# spacing technical replicates further from leaf
labs(y = "Genetic distance (1 - IBS)") +
guides(fill = guide_legend(override.aes = list(shape = 22, size = 10), ncol = 2), shape = guide_legend(override.aes = list(size = 8), ncol = 1, order = 1)) +
coord_cartesian(xlim = c(5, 218)) +
theme_classic()
dendNoClone = dendNoCloneA + theme(
axis.title.x = element_blank(),
axis.text.x = element_blank(),
axis.line.x = element_blank(),
axis.ticks.x = element_blank(),
axis.title.y = element_text(size = 24, color = "black", angle = 90),
axis.text.y = element_text(size = 20, color = "black"),
axis.line.y = element_line(),
axis.ticks.y = element_line(),
panel.grid = element_blank(),
panel.border = element_blank(),
panel.background = element_blank(),
legend.key = element_blank(),
legend.title = element_text(size = 24),
legend.text = element_text(size = 20),
legend.position = "bottom")
# dendNoClone
ma = as.matrix(read.table("../data/snps/sintFiltSnps.ibsMat")) # reads in IBS matrix produced by ANGSD
dimnames(ma) = list(pcadmix[,9],pcadmix[,9])
pops = pcadmix$pop
depth = pcadmix$depth
cluster = pcadmix$cluster
dend = ma %>% as.dist() %>% hclust(.,"ave") %>% as.dendrogram()
dData = dend %>% dendro_data()
# Making the branches hang shorter so we can easily see clonal groups
dData$segments$yend2 = dData$segments$yend
for(i in 1:nrow(dData$segments)) {
if (dData$segments$yend2[i] == 0) {
dData$segments$yend2[i] = (dData$segments$y[i] - 0.01)}}
dendPoints = dData$labels
dendPoints$pop = pops[order.dendrogram(dend)]
dendPoints$depth = depth[order.dendrogram(dend)]
dendPoints$cluster = cluster[order.dendrogram(dend)]
rownames(dendPoints) = dendPoints$label
# Making points at the leaves to place symbols for populations
point = as.vector(NA)
for(i in 1:nrow(dData$segments)) {
if (dData$segments$yend[i] == 0) {
point[i] = dData$segments$y[i] - 0.01
} else {
point[i] = NA}}
dendPoints$y = point[!is.na(point)]
dendPoints$depth = factor(dendPoints$depth)
dendPoints$depth = factor(dendPoints$depth, levels(dendPoints$depth)[c(2,1)])
dendPoints$pop = factor(dendPoints$pop)
dendPoints$pop = factor(dendPoints$pop, levels(dendPoints$pop)[c(4, 1, 3, 2)])
dendPoints$cluster = factor(dendPoints$cluster)
dendLA = ggplot() +
geom_segment(data = segment(dData), aes(x = x, y = y, xend = xend, yend = yend2), size = 0.5) +
geom_point(data = dendPoints, aes(x = x, y = y, fill = cluster, shape = depth), size = 4, stroke = 0.25) +
scale_fill_manual(values = kColPal, name= "Lineage:")+
scale_shape_manual(values = c(21, 23), name = "Depth Zone:")+
# spacing technical replicates further from leaf
labs(y = "Genetic distance (1 - IBS)") +
guides(fill = guide_legend(override.aes = list(shape = 22, size = 10), ncol = 3), shape = guide_legend(override.aes = list(size = 8), ncol = 1, order = 1)) +
coord_cartesian(xlim = c(5, 218)) +
theme_classic()
dendL = dendLA + theme(
axis.title.x = element_blank(),
axis.text.x = element_blank(),
axis.line.x = element_blank(),
axis.ticks.x = element_blank(),
axis.title.y = element_text(size = 24, color = "black", angle = 90),
axis.text.y = element_text(size = 20, color = "black"),
axis.line.y = element_line(),
axis.ticks.y = element_line(),
panel.grid = element_blank(),
panel.border = element_blank(),
panel.background = element_blank(),
legend.key = element_blank(),
legend.title = element_text(size = 24),
legend.text = element_text(size = 20),
legend.position = "bottom")
# dendLDendrogram plots
dendPlots = (cloneDend / dendNoClone / dendL) +
plot_annotation(tag_levels = 'A') +
plot_layout(guides = "collect") &
theme(plot.tag = element_text(size = 32),
legend.position = "bottom")
ggsave("../figures/figureS1.png", plot = dendPlots, height = 19.5, width = 35, units = "in", dpi = 300)
ggsave("../figures/figureS1.svg", plot = dendPlots, height = 19.5, width = 35, units = "in", dpi = 300)
Population structure
PCA
cov = read.table("../data/snps/pcangsd/fkSintPcangsd3x.cov") %>% as.matrix()
pcAdmix = read.table("../data/snps/k/Clumpp3xK4.output") %>% dplyr::select(V6, V7, V8, V9)
pcAdmix %>% summarise(sum(V6),sum(V7), sum(V8), sum(V9)) ## sum(V6) sum(V7) sum(V8) sum(V9)
## 1 128.7501 43.4095 31.2726 16.5678pcAdmix = pcAdmix %>% rename("cluster1" = "V6", "cluster2" = "V7", "cluster3" = "V8", "cluster4" = "V9") %>%dplyr::select(order(colnames(.)))
pcaEig = eigen(cov)
sintPcaVar = pcaEig$values/sum(pcaEig$values)*100
head(sintPcaVar)## [1] 8.8597260 3.9773927 3.4518340 0.6623574 0.5049635 0.4945946pcangsd = read.csv("../data/stephanocoeniaMetaData.csv")[-c(66, 68, 164, 166, 209, 211),] %>% dplyr::select("sample" = tubeID, "pop" = site, "depth" = depthZone, "depthm" = depthM)
pcangsd$popdepth = as.factor(paste(pcangsd$pop, pcangsd$depth, sep = " "))
pcangsd$popdepth = factor(pcangsd$popdepth, levels(pcangsd$popdepth)[c(4, 3, 6, 5, 2, 1, 8, 7)])
pcangsd$pop = factor(pcangsd$pop)
pcangsd$pop = factor(pcangsd$pop, levels(pcangsd$pop)[c( 4, 1, 3, 2)])
pcangsd$depth = factor(pcangsd$depth)
pcangsd$depth = factor(pcangsd$depth, levels(pcangsd$depth)[c(2, 1)])
pcangsd$PC1 = pcaEig$vectors[,1]
pcangsd$PC2 = pcaEig$vectors[,2]
pcangsd$PC3 = pcaEig$vectors[,3]
pcangsd$PC4 = pcaEig$vectors[,4]
pcangsdClust = pcAdmix %>% mutate(cluster = ifelse(cluster1 < 0.75 & cluster2 < 0.75 & cluster3 < 0.75 & cluster4 < 0.75, "NA", ifelse(cluster1 >=0.75, 1, ifelse(cluster2 >= 0.75, 2, ifelse(cluster3 >= 0.75, 3,ifelse(cluster4 >= 0.75, 4, 0))))))
# pcangsdClust$clusterX = as.vector(d_clust$classification)
pcangsd = pcangsd %>% mutate(pcangsdClust)
pcangsd$cluster = as.factor(pcangsd$cluster)
levels(pcangsd$cluster) = c("Blue", "Teal", "Green", "Yellow", "Admixed")
bamsClusters = pcangsd %>% dplyr::select(sample, cluster) %>% dplyr::arrange(sample)
bamsSamples = read.delim("../data/snps/bamsNoClones", header = FALSE)
bamsClusters$sample = bamsSamples$V1
# bamsClusters = as.data.frame(bamsClusters)
write.table(x = bamsClusters, file = "../data/snps/bamsClusters", sep = "\t", row.names = FALSE, col.names = FALSE, quote = FALSE)
pcangsd = merge(pcangsd, aggregate(cbind(mean.x = PC1, mean.y = PC2)~popdepth, pcangsd, mean), by="popdepth")pcangsd %>% group_by(depth,cluster) %>% summarize(n = n())## `summarise()` has grouped output by 'depth'. You can override using the `.groups`
## argument.## # A tibble: 9 × 3
## # Groups: depth [2]
## depth cluster n
## <fct> <fct> <int>
## 1 Shallow Blue 50
## 2 Shallow Teal 25
## 3 Shallow Green 29
## 4 Shallow Yellow 15
## 5 Shallow Admixed 1
## 6 Mesophotic Blue 81
## 7 Mesophotic Teal 15
## 8 Mesophotic Green 2
## 9 Mesophotic Admixed 2Plot PCA
pcaTheme = theme(axis.title.x = element_text(color = "black", size = 10),
axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
axis.line.x = element_blank(),
axis.title.y = element_text(color = "black", size = 10),
axis.text.y = element_blank(),
axis.ticks.y = element_blank(),
axis.line.y = element_blank(),
legend.position = "none",
legend.title = element_text(size = 8),
legend.text = element_text(size = 8),
legend.key.size = unit(5, "pt"),
panel.border = element_rect(color = "black", size = 1),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank())
pcaPlot12SA = ggplot() +
geom_hline(yintercept = 0, color = "gray90", size = 0.25) +
geom_vline(xintercept = 0, color = "gray90", size = 0.25) +
geom_point(data = pcangsd, aes(x = PC1, y = PC2, fill = pop, shape = depth, color = pop), stroke = 0, size = 2.5, alpha = 0.5, show.legend = FALSE) +
geom_point(data = pcangsd, aes(x = mean.x, y = mean.y, fill = pop, shape = depth), color = "black", size = 2.75, alpha = 1, stroke = 0.25) +
scale_shape_manual(values = c(21, 23), name = "Depth Zone") +
scale_fill_manual(values = flPal, name = "Site") +
scale_color_manual(values = flPal, name = "Site") +
labs(x = paste0("PC 1 (", format(round(sintPcaVar[1], 1), nsmall = 1)," %)"), y = paste0("PC 2 (", format(round(sintPcaVar[2], 1), nsmall = 1), " %)")) +
guides(shape = guide_legend(override.aes = list(size = 2, stroke = 0.25, alpha = NA), order = 2, ncol = 1), fill = guide_legend(override.aes = list(shape = 22, size = 2, fill = flPal, alpha = NA), order = 1, ncol = 1)) +
theme_bw()
pcaPlot12S = pcaPlot12SA +
pcaTheme +
theme(legend.position = c(0.17, 0.23))
pcaPlot12S
pcaPlot12LA = ggplot() +
geom_hline(yintercept = 0, color = "gray90", size = 0.5) +
geom_vline(xintercept = 0, color = "gray90", size = 0.5) +
geom_point(data = pcangsd, aes(x = PC1, y = PC2, fill = cluster, shape = depth), color = "black", size = 2, alpha = 1, show.legend = TRUE) +
scale_shape_manual(values = c(21, 23), name = "Depth Zone") +
scale_fill_manual(values = kColPal, name = "Lineage") +
labs(x = paste0("PC 1 (", format(round(sintPcaVar[1], 1), nsmall = 1)," %)"), y = paste0("PC 2 (", format(round(sintPcaVar[2], 1), nsmall = 1), " %)")) +
guides(shape = "none", fill = guide_legend(override.aes = list(shape = 22, size = 2, fill = kColPal, alpha = NA), order = 1, ncol = 1))+
theme_bw()
pcaPlot12L = pcaPlot12LA +
pcaTheme +
theme(legend.position = c(0.12,0.15))
pcaPlot23LA = ggplot() +
geom_hline(yintercept = 0, color = "gray90", size = 0.5) +
geom_vline(xintercept = 0, color = "gray90", size = 0.5) +
geom_point(data = pcangsd, aes(x = PC3, y = PC2, fill = cluster, shape = depth), color = "black", size = 2, alpha = 1, show.legend = TRUE) +
scale_shape_manual(values = c(21, 23), name = "Depth Zone") +
scale_fill_manual(values = kColPal, name = "Lineage") +
labs(x = paste0("PC 3 (", format(round(sintPcaVar[3], 1), nsmall = 1)," %)"), y = paste0("PC 2 (", format(round(sintPcaVar[2], 1), nsmall = 1), " %)")) +
guides(shape = guide_legend(override.aes = list(size = 2, stroke = 0.5, alpha = NA), order = 2, ncol = 1), fill = guide_legend(override.aes = list(shape = 22, size = 2, fill = kColPal, alpha = NA), order = 1, ncol = 1, byrow = TRUE))+
theme_bw()
pcaPlot23L = pcaPlot23LA +
pcaThemePut all plots together
pcaPlots = ((pcaPlot12S + theme(axis.title.y = element_text(margin = ggplot2::margin(r = -20, unit = "pt")))) | pcaPlot12L | pcaPlot23L) +
plot_annotation(tag_levels = 'A') &
theme(plot.tag = element_text(size = 18),
panel.background = element_rect(fill = "white"),
legend.spacing = unit(-5, "pt"),
legend.key = element_blank(),
legend.background = element_blank())
pcaPlots
Admixture
Prepare admixture outputs for plotting
fkSintAdmix = pcangsd %>%dplyr::select(-PC1, -PC2, -PC3, -PC4, -cluster, -depthm, -mean.x, -mean.y)
fkSintAdmix$pop = factor(fkSintAdmix$pop, levels(fkSintAdmix$pop)[c( 4, 3, 2, 1)])
fkSintAdmix = arrange(fkSintAdmix, pop, depth, -cluster1, -cluster2, cluster4)
popCounts = fkSintAdmix %>% group_by(pop, depth) %>% summarise(n = n())## `summarise()` has grouped output by 'pop'. You can override using the `.groups`
## argument.popCounts## # A tibble: 8 × 3
## # Groups: pop [4]
## pop depth n
## <fct> <fct> <int>
## 1 Riley's Hump Shallow 30
## 2 Riley's Hump Mesophotic 15
## 3 Tortugas Bank Shallow 30
## 4 Tortugas Bank Mesophotic 25
## 5 Lower Keys Shallow 30
## 6 Lower Keys Mesophotic 30
## 7 Upper Keys Shallow 30
## 8 Upper Keys Mesophotic 30popCountList = reshape2::melt(lapply(popCounts$n,function(x){c(1:x)}))
fkSintAdmix$ord = popCountList$value
fkSintAdmixMelt = melt(fkSintAdmix, id.vars=c("sample", "pop", "depth", "popdepth", "ord"), variable.name="Ancestry", value.name="Fraction")
fkSintAdmixMelt$Ancestry = factor(fkSintAdmixMelt$Ancestry)
fkSintAdmixMelt$Ancestry = factor(fkSintAdmixMelt$Ancestry, levels = rev(levels(fkSintAdmixMelt$Ancestry)))
popAnno = data.frame(x1 = c(0.5, 0.5, 0.5, 0.5), x2 = c(30.5, 30.5, 30.5, 30.5),
y1 = -0.1, y2 = -0.1, sample = NA, Ancestry = NA, depth = "Mesophotic",
ord = NA, Fraction = NA,
pop = c("Riley's Hump", "Tortugas Bank",
"Lower Keys", "Upper Keys"))
popAnno$pop = factor(popAnno$pop)
popAnno$pop = factor(popAnno$pop, levels = levels(popAnno$pop)[c(4, 1, 3, 2)])Make admixture plots
admixPlotA = ggplot(data = fkSintAdmixMelt, aes(x = ord, y = Fraction, fill = Ancestry, order = sample)) +
geom_segment(data = popAnno, aes(x = x1, xend = x2, y = -.12, yend = -.12, color = pop), size = 7) +
geom_bar(stat = "identity", position = "fill", width = 1, colour = "grey25", size = 0.2) +
facet_grid(factor(depth) ~ pop, switch = "both") +
geom_text(data = (fkSintAdmixMelt %>% filter(depth == "Mesophotic", pop %in% c("Riley's Hump", "Tortugas Bank"), sample %in% c(
"SFK001", "SFK100"), Ancestry == "cluster1")), x = 15.5, y = -.1, aes(label = pop), size = 4, color = "#FFFFFF") +
geom_text(data = (fkSintAdmixMelt %>% filter(depth == "Mesophotic", pop %in% c("Lower Keys", "Upper Keys"), sample %in% c(
"SFK101", "SFK201"), Ancestry == "cluster1")), x = 15.5, y = -.1, aes(label = pop), size = 3.5, color = "#000000") +
scale_fill_manual(values = kColPal) +
scale_color_manual(values = flPal) +
scale_x_discrete(expand = c(0.005, 0.005)) +
scale_y_continuous(expand = c(0.001, 0.001)) +
coord_cartesian(ylim = c(0.0, 1.0), clip = "off") +
theme_bw()
admixPlot = admixPlotA +
theme_bw()+
theme(
panel.grid = element_blank(),
panel.background = element_rect(fill = "gray70"),
plot.background = element_blank(),
panel.border = element_rect(fill = NA, color = "black", size = 0.75, linetype = "solid"),
panel.spacing.x = grid:::unit(0.05, "lines"),
panel.spacing.y = grid:::unit(0.05, "lines"),
axis.text.x = element_blank(),
axis.text.y = element_blank(),
axis.ticks.x = element_blank(),
axis.ticks.y = element_blank(),
axis.title = element_blank(),
strip.background.x = element_blank(),
strip.background.y = element_blank(),
strip.text = element_text(size = 8),
strip.text.y.left = element_text(size = 10, angle = 90),
strip.text.x.bottom = element_text(vjust = 1, color = NA),
legend.key = element_blank(),
legend.position = "none",
legend.title = element_text(size = 8))
admixPlot
Lineage demographics
Depth distribution of lineages
leveneTest(lm(depthm ~ cluster, data = subset(pcangsd, subset = pcangsd$cluster!="Admixed")))## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 3 6.9537 0.000174 ***
## 213
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1depthAov = welch_anova_test(depthm ~ cluster, data = subset(pcangsd, subset = pcangsd$cluster!="Admixed"))
dF = depthAov$statistic[[1]]
depthPH = games_howell_test(depthm ~ cluster, data = subset(pcangsd, subset = pcangsd$cluster!="Admixed"), conf.level = 0.95) %>% as.data.frame()
depthLetters = data.frame(x = factor(c("Blue", "Teal", "Green", "Yellow")), y = c(2.5, 2.5, 2.5, 2.5), grp = c("a", "b", "bc", "c"))
lineageViolinA = ggplot(data = subset(pcangsd, subset = pcangsd$cluster!="Admixed"), aes(x = cluster, y = depthm)) +
annotate(geom = "rect", xmin = -Inf, xmax = Inf, ymin = 30, ymax = Inf, fill = "black", alpha = 0.15, color = NA) +
geom_beeswarm(aes(fill = cluster), shape = 21, size = 2, cex = 1.5, alpha = 0.5) +
geom_violin(aes(fill = cluster),adjust = 1, linewidth = 0, color = "black", alpha = 0.35, width = 0.9, trim = F, scale = "width") +
geom_violin(adjust = 1, linewidth = 0.4, color = "black", alpha = 1, width = 0.9, trim = F, fill = NA, scale = "width") +
geom_boxplot(width = 0.2, color = "black", fill = "white", outlier.colour = NA, linewidth = 0.6, alpha = 0.5) +
geom_text(data = depthLetters, aes(x = x, y = y, label = grp), size = 4) +
annotate(geom = "text", x = 3.75, y =50, label = bquote(italic("F")~" = "~.(dF)*"; "~italic("p")~" < 0.001"), size = 3) +
scale_fill_discrete(type = kColPal, name = "Lineage") +
scale_color_discrete(type = kColPal, name = "Lineage") +
xlab("Lineage") +
ylab("Depth (m)") +
scale_y_reverse(breaks = seq(5, 50, 5)) +
theme_bw()
lineageViolin = lineageViolinA + theme(
axis.title = element_text(color = "black", size = 12),
axis.text = element_text(color = "black", size = 10),
legend.position = "none",
legend.key.size = unit(0.3, 'cm'),
panel.border = element_rect(color = "black", size = 1),
panel.background = element_blank(),
plot.background = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank())
lineageViolin
Lineage differentiation
Measuring with global weighted FST calculated from SFS
First prepare and sort FST for plotting
pop.order = c("Blue", "Teal", "Green", "Yellow")
# reads in fst
fstMa1 <- read.delim("../data/snps/sintKFst3x.out") %>% dplyr::select(-fst) %>% df_to_pw_mat(., "pop1", "pop2", "weightedFst")
fstMa1## Blue Teal Green Yellow
## Blue 0.000000 0.047964 0.185205 0.162474
## Teal 0.047964 0.000000 0.210652 0.184036
## Green 0.185205 0.210652 0.000000 0.209234
## Yellow 0.162474 0.184036 0.209234 0.000000fstMa = fstMa1
upperTriangle(fstMa, byrow = TRUE) <- lowerTriangle(fstMa)
fstMa <- fstMa[,pop.order] %>%
.[pop.order,]
fstMa[upper.tri(fstMa)] <- NA
fstMa <- as.data.frame(fstMa)
# rearrange and reformat matrix
fstMa$Pop = factor(row.names(fstMa), levels = unique(pop.order))
# melt matrix data (turn from matrix into long dataframe)
fst = melt(fstMa, id.vars = "Pop", value.name = "Fst", variable.name = "Pop2", na.rm = FALSE)
fst$Fst = round(fst$Fst, 3)
fst$site = fst$Pop
fst$site = factor(gsub("\\n.*", "", fst$site))
fst$site = factor(fst$site, levels = levels(fst$site)[c(1, 3, 2, 4)])
fst$site2 = fst$Pop2
fst$site2 = factor(gsub("\\n.*", "", fst$site2))
fst$site2 = factor(fst$site2, levels = levels(fst$site2)[c(1, 3, 2, 4)])
fst$Pop2 = factor(fst$Pop2, levels = levels(fst$Pop2)[c(4, 3, 2, 1)])Construct a heatmap of FST values
fstHeatmapA = ggplot(data = fst %>% filter(Fst != 0), aes(Pop, Pop2, fill = as.numeric(as.character(Fst)))) +
geom_tile(color = "white") +
geom_segment(data = fst, aes(x = 0.475, xend = 0.15, y = Pop2, yend = Pop2, color = site2), size = 21.25) + #colored boxes for Y-axis labels
geom_segment(data = fst, aes(x = Pop, xend = Pop, y = 0.2, yend = 0.475, color = site), size = 25) + #colored boxes for X-axis labels
scale_color_manual(values = kColPal, guide = NULL) +
# scale_fill_gradient(low = "gray95", high = "gray35", limit = c(0, 0.22), space = "Lab", name = expression(paste(italic("F")[ST])), na.value = NA, guide = "colourbar") +
# scale_fill_gradientn(colours = c("white", paletteer_c("viridis::mako", n = 20)[13:1]), limit = c(0,0.25), space = "Lab", name = expression(paste(italic("F")["ST"]), na.value = "transparent")) +
scale_fill_gradientn(colours = paletteer_c("viridis::mako", n = 10, direction = -1)[c(1:7)], limit = c(0, 0.22), space = "Lab", name = expression(paste(italic("F")["ST"]), na.value = "transparent")) +
# scale_fill_paletteer_c(`"viridis::mako"`, direction = -1) +
geom_text(data = fst %>% filter(Fst != 0), aes(Pop, Pop2, label = Fst), color = "black", size = 3.5, fontface = "bold") +
guides(fill = guide_colorbar(barwidth = 7.5, barheight = 0.75, title.position = "top", title.hjust = 0.5, direction = "horizontal", ticks.colour = "black", frame.colour = "black")) +
scale_y_discrete(position = "left", limits = rev(levels(fst$Pop2))) +
scale_x_discrete(limits = levels(fst$Pop2)[c(1:4)]) +
coord_cartesian(xlim = c(1, 4), ylim = c(1, 4), clip = "off") +
theme_minimal()
fstHeatmap = fstHeatmapA + theme(
axis.text.x = element_text(vjust = 3.5, size = 10, hjust = 0.5, color = "black"),
axis.text.y = element_text(size = 10, color = "black", angle = 90, hjust = 0.5, vjust = -1.5),
axis.title.x = element_blank(),
axis.title.y = element_blank(),
panel.grid.major = element_blank(),
panel.border = element_blank(),
axis.ticks = element_blank(),
legend.title = element_text(size = 8, color = "black"),
legend.text = element_text(size = 8, color = "black"),
legend.position = c(0.6, 0.9),
plot.background = element_blank(),
panel.background = element_blank(),
)
fstHeatmap
Lineage demographics through time
Making stairway plot of effective population sizes of each lineage throughout time
bl = read.table("../data/snps/sintBlue.final.summary", header = TRUE) %>% mutate("Lineage" = "Blue")
tl = read.table("../data/snps/sintTeal.final.summary", header = TRUE) %>% mutate("Lineage" = "Teal")
gn = read.table("../data/snps/sintGreen.final.summary", header = TRUE) %>% mutate("Lineage" = "Green")
yl = read.table("../data/snps/sintYellow.final.summary", header = TRUE) %>% mutate("Lineage" = "Yellow")
swData = rbind(bl, tl, gn, yl)
swData$Lineage = factor(swData$Lineage)
swData$Lineage = factor(swData$Lineage, levels = levels(swData$Lineage)[c(1,3,2,4)])Constuct plot
swPlotA = ggplot(data = swData, aes(x = year, y = Ne_median, ymin = Ne_12.5., ymax = Ne_87.5., color = Lineage, fill = Lineage)) +
geom_ribbon(color = NA, aes(alpha = Lineage)) +
# geom_line(size = 0.6) +
geom_line(linewidth = 1.15) +
scale_fill_manual(values = kColPal[c(1:4)]) +
scale_color_manual(values = kColPal[c(1:4)]) +
scale_alpha_manual(values = c(0.25, 0.25, 0.35, 0.4)) +
scale_x_continuous(name = "KYA", limits = c(0,5.25e5), breaks = c(1e5,2e5,3e5,4e5,5e5), labels = c("100","200", "300", "400", "500")) +
scale_y_continuous(name = bquote(italic(N[e])~"(x10"^"3"*")"), limits = c(0,14e5), breaks = c(2.5e5,5e5,7.5e5,10e5,12.5e5), labels = c("250","500", "750", "1000", "1250"))+
coord_cartesian(xlim = c(5.25e5, 0), expand = FALSE) +
theme_bw()
swPlot = swPlotA + theme(
axis.title = element_text(color = "black", size = 12),
axis.text = element_text(color = "black", size = 10),
legend.key.size = unit(0.3, 'cm'),
legend.title = element_text(color = "black", size = 12),
legend.text = element_text(color = "black", size = 12),
legend.position = "none",
# legend.position = c(0.85, 0.82),
plot.background = element_blank(),
panel.background = element_blank(),
panel.border = element_rect(size = 1),
panel.grid = element_blank()
)
swPlot
Heterozygosity and Inbreeding
popData = read.csv("../data/stephanocoeniaMetaData.csv")[-c(66, 68, 164, 166, 209, 211),] %>% dplyr::select("sample" = tubeID, "Site" = site, "Depth" = depthZone, "depthm" = depthM) # Reads in population data
popData$a = c(0:219)
popData$Site = factor(popData$Site)
popData$Site = factor(popData$Site, levels = levels(popData$Site)[c(2,3,1,4)])
popData$Depth = factor(popData$Depth)
popData$Depth = factor(popData$Depth, levels = levels(popData$Depth)[c(2,1)])
sampleData = fknmsSites[-c(66,68,164,166,209,211),] %>% group_by(site, depthZone)%>% summarise(depthZone = (first(depthZone)), depthRange = paste(min(depthM), "--", max(depthM), sep = ""), meanDepth = round(mean(depthM),1), n = n())%>% droplevels() %>% as.data.frame()## `summarise()` has grouped output by 'site'. You can override using the `.groups`
## argument.# Average depth of populations
fkPopDepths = fknmsSites[-c(66,68,164,166,209,211),] %>% group_by(site, depthZone) %>% summarise(avgDepthM = mean(depthM), n = n())## `summarise()` has grouped output by 'site'. You can override using the `.groups`
## argument.fkPopDepths## # A tibble: 8 × 4
## # Groups: site [4]
## site depthZone avgDepthM n
## <fct> <fct> <dbl> <int>
## 1 Upper Keys Shallow 23.6 30
## 2 Upper Keys Mesophotic 43.8 30
## 3 Lower Keys Shallow 18.0 30
## 4 Lower Keys Mesophotic 32.8 30
## 5 Tortugas Bank Shallow 21.1 30
## 6 Tortugas Bank Mesophotic 32.0 25
## 7 Riley's Hump Shallow 26.4 30
## 8 Riley's Hump Mesophotic 33.2 15sampleTab = sampleData
colnames(sampleTab) = c("Site", "Depth zone", "Sampling \ndepth (m)", "Average \ndepth (m)", "n")
sampleTab$Site## [1] Upper Keys Upper Keys Lower Keys Lower Keys Tortugas Bank Tortugas Bank
## [7] Riley's Hump Riley's Hump
## Levels: Upper Keys Lower Keys Tortugas Bank Riley's HumpfinalTabSite = c("Upper Keys", "", "Lower Keys","", "Tortugas Bank", "", "Riley's Hump", "")
sampleTab$Site = finalTabSite
hetAll = read.table("../data/snps/sintHet3x")
colnames(hetAll) = c("sample", "He")
hetAll$sample = str_pad(hetAll$sample, 3, pad = "0")
hetAll$sample = paste("SFK",hetAll$sample, sep ="")
sintBreed = read.delim("../data/snps/fkSintF.indF", header = FALSE)
sintRelate = read.delim("../data/snps/fkSintFiltRelate3x")
sintRelate2 = sintRelate %>% group_by(a, b) %>% dplyr::select("Rab" = rab, "theta" = theta)## Adding missing grouping variables: `a`, `b`sintRelate2 = sintRelate2 %>% left_join(popData, by = "a") %>% left_join(popData, by = c("b" = "a"), suffix = c(".a", ".b"))
sintRelate2$popDepth.a = paste(sintRelate2$Site.a, sintRelate2$Depth.a, sep = " ")
sintRelate2$popDepth.b = paste(sintRelate2$Site.b, sintRelate2$Depth.b, sep = " ")
sintRelate2 = sintRelate2 %>% left_join((pcangsd %>%dplyr::select(sample, cluster)) , by = c("sample.a" = "sample")) %>% left_join((pcangsd %>%dplyr::select(sample, cluster)) , by = c("sample.b" = "sample"))
sintRelate = sintRelate2 %>% filter(cluster.x != "Admixed",cluster.x == cluster.y) %>% rename(Depth = Depth.a, Site = Site.a, cluster = cluster.x)
sintRelateMean = sintRelate %>% group_by(Site, Depth) %>% dplyr::summarize(N = n(), meanRab = mean(Rab), seRab = sd(Rab)/sqrt(N), meanTheta = mean(theta), seTheta = sd(theta)/sqrt(N)) %>% dplyr::select(-N)## `summarise()` has grouped output by 'Site'. You can override using the `.groups`
## argument.het = left_join(popData, hetAll, by = "sample") %>% mutate("inbreed" = sintBreed$V1) %>% left_join((pcangsd %>% dplyr::select(sample, cluster)) , by = "sample") %>% dplyr::select(-a)
hetStats = het %>% group_by(Site, Depth) %>% summarise(N = n(), meanAll = mean(He), sdAll = sd(He), seAll = sd(He)/sqrt(N), meanInbreed = mean(inbreed), sdInbreed = sd(inbreed), seInbreed = sd(inbreed)/sqrt(N)) %>% left_join(sintRelateMean)## `summarise()` has grouped output by 'Site'. You can override using the `.groups`
## argument.
## Joining with `by = join_by(Site, Depth)`min(hetStats$meanAll, na.rm = TRUE)## [1] 0.002376667max(hetStats$meanAll, na.rm = TRUE)## [1] 0.002664hetTab = hetStats %>% arrange(desc(Site))
hetTab$n = hetTab$N
hetTab$Ha = paste(round(hetTab$meanAll, 4), "±", round(hetTab$seAll, 5), sep = " ")
hetTab$F = paste(round(hetTab$meanInbreed, 2), "±", round(hetTab$seInbreed, 3), sep = " ")
hetTab$Rab = paste(round(hetTab$meanRab, 2), "±", round(hetTab$seRab, 3), sep = " ")
hetTab$Theta = paste(round(hetTab$meanTheta, 2), "±", round(hetTab$seTheta, 4), sep = " ")
hetTab$`Sampling \ndepth (m)` = sampleTab$`Sampling \ndepth (m)`
hetTab$`Average \ndepth (m)` = sampleTab$`Average \ndepth (m)`
hetTab = hetTab %>% dplyr::select(Site, Depth, `Sampling \ndepth (m)`, `Average \ndepth (m)`, n, Ha, F, Rab, Theta)
colnames(hetTab)[2] = "Depth \nzone"
finalTabSite = c("Upper Keys", "", "Lower Keys", "", "Tortugas Bank", "", "Riley's Hump", "")
hetTab$Site = finalTabSite
hetTabPub = hetTab %>% dplyr::select(-Theta) %>%
flextable() %>%
flextable::compose(part = "header", j = "n", value = as_paragraph(as_i("n"))) %>%
flextable::compose(part = "header", j = "Ha", value = as_paragraph(as_i("H"), as_sub("A"))) %>%
flextable::compose(part = "header", j = "F", value = as_paragraph(as_i("F"))) %>%
flextable::compose(part = "header", j = "Rab", value = as_paragraph(as_i("R"), as_i(as_sub("AB")))) %>%
flextable::font(fontname = "Times New Roman", part = "all") %>%
flextable::fontsize(size = 10, part = "all") %>%
flextable::bold(part = "header") %>%
flextable::align(align = "left", part = "all") %>%
flextable::autofit()
table2 = read_docx()
table2 = body_add_flextable(table2, value = hetTabPub)
print(table2, target = "../tables/table2.docx")
hetTabPubSite | Depth | Sampling | Average | n | HA | F | RAB |
|---|---|---|---|---|---|---|---|
Upper Keys | Shallow | 18.3--28.3 | 23.6 | 30 | 0.0024 ± 0.00004 | 0.15 ± 0.011 | 0.07 ± 0.004 |
Mesophotic | 41.8--45.4 | 43.9 | 30 | 0.0026 ± 0.00003 | 0.09 ± 0.005 | 0.03 ± 0.001 | |
Lower Keys | Shallow | 17.1--19.2 | 18.0 | 30 | 0.0024 ± 0.00003 | 0.16 ± 0.012 | 0.09 ± 0.004 |
Mesophotic | 31.4--35.1 | 32.8 | 30 | 0.0026 ± 0.00002 | 0.09 ± 0.005 | 0.03 ± 0.001 | |
Tortugas Bank | Shallow | 14.6--29.9 | 21.1 | 30 | 0.0025 ± 0.00005 | 0.15 ± 0.012 | 0.08 ± 0.002 |
Mesophotic | 30.2--35.1 | 32.0 | 25 | 0.0027 ± 0.00013 | 0.1 ± 0.008 | 0.04 ± 0.001 | |
Riley's Hump | Shallow | 25.3--28 | 26.4 | 30 | 0.0024 ± 0.00003 | 0.14 ± 0.012 | 0.06 ± 0.002 |
Mesophotic | 30.8--37.5 | 33.2 | 15 | 0.0025 ± 0.00005 | 0.11 ± 0.013 | 0.05 ± 0.002 |
Heterozygosity, diversity, and inbreeding plots
Heterozygosity across all RAD loci by lineage
leveneTest(lm(He ~ cluster, data = subset(het, subset = pcangsd$cluster!="Admixed")))## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 4 18.093 0.0000000000008437 ***
## 212
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1hetAov = welch_anova_test(He ~ cluster, data = subset(het, subset = het$cluster!="Admixed"))
hF = hetAov$statistic[[1]]
hetPH = games_howell_test(He ~ cluster, data = subset(het, subset = het$cluster!="Admixed"), conf.level = 0.95) %>% as.data.frame()
# hetLetters = data.frame(x = factor(c("Blue", "Teal", "Green", "Yellow")), y = c(0.0039, 0.0039, 0.0039, 0.0039), grp = c("a", "bc", "b", "c"))
hetLetters = data.frame(x = factor(c("Blue", "Teal", "Green", "Yellow")), y = c(0.0039, 0.0039, 0.0039, 0.0039), grp = c("a", "b", "c", "b"))
hetPlotKA = ggplot(data = het %>% filter(cluster != "Admixed"), aes(x = cluster, y = He)) +
geom_beeswarm(aes(fill = cluster), shape = 21, size = 2, cex = 0.75, alpha = 0.5) +
geom_violin(aes(fill = cluster, group = cluster), adjust = 1, linewidth = 0, color = "black", alpha = 0.35, width = 0.9, trim = F, scale = "width") +
geom_violin(aes(fill = cluster, group = cluster), adjust = 1, linewidth = 0.4, color = "black", alpha = 1, width = 0.9, trim = F, fill = NA, scale = "width") +
geom_boxplot(aes(fill = cluster, group = cluster), width = 0.2, color = "black", fill = "white", outlier.colour = NA, linewidth = 0.6, alpha = 0.5) + xlab("Lineage") +
geom_text(data = hetLetters, aes(x = x, y = y, label = grp), size = 4) +
annotate(geom = "text", x = 3.65, y =0.0036, label = bquote(italic("F")~" = "~.(hF)*"; "~italic("p")~" < 0.001"), size = 3) +
scale_fill_discrete(type = kColPal, name = "Lineage") +
xlab("Lineage") +
ylab("Heterozygosity") +
scale_y_continuous(breaks = seq(0.0022, 0.0038, 0.0004)) +
coord_cartesian(expand = TRUE, xlim = c(0.85, 4)) +
theme_bw()
hetPlotK = hetPlotKA + theme(
axis.title = element_text(color = "black", size = 12),
axis.text = element_text(color = "black", size = 10),
legend.position = "none",
legend.key.size = unit(0.3, 'cm'),
panel.border = element_rect(color = "black", size = 1),
panel.background = element_blank(),
plot.background = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank())
# hetPlotKMean inbreeding plot
leveneTest(lm(inbreed ~ cluster, data = subset(het, subset = pcangsd$cluster!="Admixed")))## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 4 6.3025 0.00008245 ***
## 212
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ibAov = welch_anova_test(inbreed ~ cluster, data = subset(het, subset = het$cluster!="Admixed"))
iF = ibAov$statistic[[1]]
ibPH = games_howell_test(inbreed ~ cluster, data = subset(het, subset = het$cluster!="Admixed"), conf.level = 0.95) %>% as.data.frame()
inbreedLetters = data.frame(x = factor(c("Blue", "Teal", "Green", "Yellow")), y = c(0.38, 0.38, 0.38, 0.38), grp = c("a", "b", "c", "c"))
inbreedingPlot = ggplot(data = het %>% filter(cluster!="Admixed"), aes(x = cluster, y = inbreed)) +
geom_beeswarm(aes(fill = cluster), shape = 21, size = 2, cex = 1.5, alpha = 0.5) +
geom_violin(aes(fill = cluster), adjust = 1, linewidth = 0, color = "black", alpha = 0.35, width = 0.9, trim = F, scale = "width") +
geom_violin(adjust = 1, linewidth = 0.4, color = "black", alpha = 1, width = 0.9, trim = F, fill = NA, scale = "width") +
geom_boxplot(aes(fill = cluster),width = 0.2, color = "black", fill = "white", outlier.colour = NA, linewidth = 0.6, alpha = 0.5) +
geom_text(data = inbreedLetters, aes(x = x, y = y, label = grp), size = 4) +
annotate(geom = "text", x = 3.6, y =0.032, label = bquote(italic("F")~" = "~.(iF)*"; "~italic("p")~" < 0.001"), size = 3) +
xlab("Lineage") +
ylab(bquote(~"Inbreeding coefficient ("*italic(F)*")")) +
scale_fill_manual(values = kColPal) +
scale_color_manual(values = kColPal) +
scale_y_continuous(breaks=seq(0, 0.4, by = .05)) +
coord_cartesian(expand = TRUE, xlim = c(0.78, 4)) +
theme_bw() +
theme(legend.position = "none",
axis.text = element_text(size = 10, color = "black"),
axis.title = element_text(size = 12, color = "black"),
panel.border = element_rect(color = "black", size = 1),
plot.background = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank())
inbreedingPlot
Nucleotide diversity plot
# mean depth for lineages
subset(pcangsd, subset = pcangsd$cluster!="Admixed") %>% group_by(cluster) %>% summarize(depth = mean(depthm))## # A tibble: 4 × 2
## cluster depth
## <fct> <dbl>
## 1 Blue 31.4
## 2 Teal 26.3
## 3 Green 22.5
## 4 Yellow 20.2npgList = list(read_tsv("../data/snps/blue3x.thetas.idx.pestPG") %>% mutate(lineage = "Blue", depth = 31.4),
read_tsv("../data/snps/teal3x.thetas.idx.pestPG") %>% mutate(lineage = "Teal", depth = 26.3),
read_tsv("../data/snps/green3x.thetas.idx.pestPG") %>% mutate(lineage = "Green", depth = 22.5),
read_tsv("../data/snps/yellow3x.thetas.idx.pestPG")%>% mutate(lineage = "Yellow", depth = 20.2))## Rows: 30 Columns: 14
## ── Column specification ────────────────────────────────────────────────────────────────
## Delimiter: "\t"
## chr (2): #(indexStart,indexStop)(firstPos_withData,lastPos_withData)(WinStart,WinSt...
## dbl (12): WinCenter, tW, tP, tF, tH, tL, Tajima, fuf, fud, fayh, zeng, nSites
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
## Rows: 30 Columns: 14
## ── Column specification ────────────────────────────────────────────────────────────────
## Delimiter: "\t"
## chr (2): #(indexStart,indexStop)(firstPos_withData,lastPos_withData)(WinStart,WinSt...
## dbl (12): WinCenter, tW, tP, tF, tH, tL, Tajima, fuf, fud, fayh, zeng, nSites
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
## Rows: 30 Columns: 14
## ── Column specification ────────────────────────────────────────────────────────────────
## Delimiter: "\t"
## chr (2): #(indexStart,indexStop)(firstPos_withData,lastPos_withData)(WinStart,WinSt...
## dbl (12): WinCenter, tW, tP, tF, tH, tL, Tajima, fuf, fud, fayh, zeng, nSites
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
## Rows: 30 Columns: 14
## ── Column specification ────────────────────────────────────────────────────────────────
## Delimiter: "\t"
## chr (2): #(indexStart,indexStop)(firstPos_withData,lastPos_withData)(WinStart,WinSt...
## dbl (12): WinCenter, tW, tP, tF, tH, tL, Tajima, fuf, fud, fayh, zeng, nSites
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.piAll = purrr::reduce(npgList, rbind) %>%
group_by(lineage) %>%
mutate(tPps = tP/nSites) %>%
summarize(tPps = mean(tPps), depth = min(depth))
piAll$lineage = as.factor(piAll$lineage)
piAll$lineage = factor(piAll$lineage, levels(piAll$lineage)[c(1, 3, 2, 4)])
lmpi = lm(tPps~depth, data=piAll)
summary(lmpi)##
## Call:
## lm(formula = tPps ~ depth, data = piAll)
##
## Residuals:
## 1 2 3 4
## 0.00009393 -0.00028006 -0.00006686 0.00025299
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.00355804 0.00083771 4.247 0.0512 .
## depth 0.00006938 0.00003291 2.108 0.1696
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.000279 on 2 degrees of freedom
## Multiple R-squared: 0.6896, Adjusted R-squared: 0.5345
## F-statistic: 4.444 on 1 and 2 DF, p-value: 0.1696r2 = round(summary(lmpi)$r.squared, 3)
nuclDivPlot = ggplot(piAll, aes(x = depth, y = tPps)) +
geom_smooth(se = FALSE, color = 'black', method='lm', linewidth = 0.75) +
geom_point(aes(fill = lineage),shape = 21, size = 3) +
scale_color_manual(values = kColPal) +
scale_fill_manual(values = kColPal) +
labs(x='Depth (m)', y = bquote("Nucleotide diversity ("*pi*")"), shape = 'Lineage') +
annotate(geom = "text", x = 30, y = 0.0048, label = bquote(italic(R^2)~"="~.(r2)), size = 3) +
theme_bw() +
theme(axis.title.y = element_text(color = "black", size = 12),
axis.title.x = element_text(color = "black", size = 12),
axis.text = element_text(color = "black", size = 10),
legend.position = "none",
legend.key.size = unit(0.3, 'cm'),
panel.border = element_rect(color = "black", size = 1),
panel.background = element_blank(),
plot.background = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank())
nuclDivPlot## `geom_smooth()` using formula = 'y ~ x'
Lineage plots
lineagePlots = (lineageViolin | hetPlotK | inbreedingPlot) / (nuclDivPlot | swPlot | fstHeatmap) +
plot_annotation(tag_levels = "A") &
theme(plot.tag = element_text(size = 14))
# lineagePlots
ggsave("../figures/figure3.png", plot = lineagePlots, height = 7, width = 12, units = "in", dpi = 300)## `geom_smooth()` using formula = 'y ~ x'ggsave("../figures/figure3.svg", plot = lineagePlots, height = 7, width = 12, units = "in", dpi = 300)## `geom_smooth()` using formula = 'y ~ x'Outliers
#average depths
depthsBayeScan = pcadmix %>% filter(cluster != "Admixed") %>% dplyr::mutate(linDepth = paste(cluster, depth, sep = "_")) %>% group_by(linDepth) %>% summarize(meanDepth = mean(depthm))
depthsBayeScan## # A tibble: 7 × 2
## linDepth meanDepth
## <chr> <dbl>
## 1 Blue_Mesophotic 36.2
## 2 Blue_Shallow 23.7
## 3 Green_Mesophotic 31.4
## 4 Green_Shallow 21.9
## 5 Teal_Mesophotic 35.2
## 6 Teal_Shallow 21.0
## 7 Yellow_Shallow 20.2bayescan = read.table("../data/snps/fkSint3x.baye_fst.txt",header=T) %>% mutate(loc = rownames(.), out.05 = ifelse(qval < 0.05, 1, 0), out.1 = ifelse(qval < 0.1, 1, 0))
bayescan[bayescan[, 3]<=0.0001, 3] = 0.0001
bayeScEnv = read.table("../data/snps/fkSint3x.bayeS_fst.txt", header=T) %>% filter(qval_g < 0.05) %>% mutate(loc = rownames(.), depthOut = 1) %>% dplyr::select(loc, depthOut)
bayescan = bayescan %>% left_join(bayeScEnv) ## Joining with `by = join_by(loc)`bayescan$depthOut = bayescan$depthOut %>% replace_na(0)
sum(bayescan$out.05)## [1] 120sum(bayescan$out.1)## [1] 157sum(bayescan$depthOut)## [1] 0for(i in 1:nrow(bayescan)){
if(bayescan$depthOut[i] == 1){
bayescan$out.05[i] = 2
}
}
bayescanPlotA = ggplot(data = bayescan, aes(x = log10(qval), y = fst, color = as.factor(out.05), alpha = as.factor(out.05))) +
geom_point(size = 1) +
geom_vline(xintercept = log10(0.05), linetype = 2, color = purple) +
xlab(expression(log[10]*"("*italic("q")*"-value)")) +
ylab(expression(italic("F")[ST])) +
scale_x_reverse() +
scale_color_manual(values = c("grey45", purple, pink)) +
scale_alpha_manual(values = c(0.25, 0.25, 0.5)) +
theme_bw()
bayescanPlot = bayescanPlotA +
theme(axis.title.x = element_text(color = "black", size = 12),
axis.text.x = element_text(color = "black", size = 12),
axis.ticks.x = element_line(color = "black"),
axis.title.y = element_text(color = "black", size = 12),
axis.text.y = element_text(color = "black", size = 10),
axis.ticks.y = element_line(color = "black"),
legend.position = "none",
legend.key.size = unit(0.3, 'cm'),
panel.border = element_rect(color = "black"),
panel.background = element_rect(fill = "white"),
plot.background = element_rect(fill = "white"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank())
# bayescanPlot
ggsave("../figures/figureS2.svg", plot = bayescanPlot, width = 14, height = 8, units = "cm", dpi = 300)
ggsave("../figures/figureS2.png", plot = bayescanPlot, width = 14, height = 8, units = "cm", dpi = 300)
Isolation by distance and redundancy analysis
S. intersepta isolation by distance
Mantel’s test
# Isolation by distance
library(geosphere)
#Get the geographic distances in km
coords = read.csv("../data/stephanocoeniaMetaData.csv")[-c(66, 68, 164, 166, 209, 211),] %>%
dplyr::select(longDD, latDD)
dGeo = as.dist((distm(coords, fun = distGeo)/1000), diag = TRUE)
snpDist = as.dist(read.table("../data/snps/sintFiltSnps.ibsMat"), diag = TRUE)
# Test IBD
set.seed(694)
snpIBD = mantel.randtest(dGeo, snpDist)
snpIBD## Monte-Carlo test
## Call: mantel.randtest(m1 = dGeo, m2 = snpDist)
##
## Observation: -0.01026129
##
## Based on 999 replicates
## Simulated p-value: 0.721
## Alternative hypothesis: greater
##
## Std.Obs Expectation Variance
## -0.6157953294 0.0001775375 0.0002873628SNP Mantel plot
snpGenDist = melt(as.matrix(snpDist), varnames = c("row", "col"), value.name = "dist")
snpGenDist = snpGenDist[snpGenDist$row != snpGenDist$col,]
geo = melt(as.matrix(dGeo), varnames = c("row", "col"), value.name = "geo")
geo = geo[geo$row != geo$col,]
snpMantelDF = data.frame(cbind(snpGenDist$dist, geo$geo))
colnames(snpMantelDF) = c("dist", "geo")
snpMantelA = ggplot(data = snpMantelDF, aes(x = geo, y = dist)) +
scale_fill_gradientn(colors = paletteer_d("wesanderson::Zissou1")) +
stat_density_2d(aes(fill = stat(density)), n = 300, contour = FALSE, geom = "raster") +
geom_smooth(method = lm, col = "black", fill = "gray40", fullrange = TRUE) +
geom_point(shape = 21, fill = "gray40", alpha = 0.25) +
scale_x_continuous(limits = c(0,300), expand = c(0,0)) +
scale_y_continuous(limits = c(0.25,0.5), breaks = seq(0.25,0.5, by = 0.05), expand = c(0,0)) +
annotate("label", x = 225, y = 0.46,
label = paste("r = ", round(snpIBD$obs, 3), "; p = ", snpIBD$pvalue),
size = 4, alpha = 0.6) +
labs(x = "Geographic distance (km)", y = expression(paste("Genetic distance "))) +
ggtitle("SNP") +
theme_bw()
snpMantel = snpMantelA + theme(
axis.title.x = element_blank(),
axis.text.x = element_text(size = 12, color = "black"),
axis.ticks.x = element_line(color = "black"),
axis.line.x = element_blank(),
axis.title.y = element_text(color = "black"),
axis.text.y = element_text(size = 12, color = "black"),
axis.ticks.y = element_line(color = "black"),
axis.line.y = element_blank(),
panel.border = element_rect(size = 1.2, color = "black"),
plot.margin = margin(0.2,0.5,0.1,0.1, unit = "cm"),
legend.position = "none")
snpMantel## `geom_smooth()` using formula = 'y ~ x'
Distance-based redundancy analysis (dbRDA)
# Set directory for sdmPredictors to download/search for previously downloaded datasets
# Also allow more than 60 seconds to download larger datasets before timing out
options(sdmpredictors_datadir="../data/snps/bioOracle", timeout = max(300, getOption("timeout")))
sintMa = as.dist(read.table("../data/snps/sintFiltSnps.ibsMat")[-131,-131])
popData = read.csv("../data/stephanocoeniaMetaData.csv")[-c(66, 68, 133, 164, 166, 209, 211),] %>% dplyr::select(sample, site, depthZone, latDD, longDD, depthM)
popData$sample = gsub("\\.[1-3]", "", popData$sample)
datasets = list_datasets(terrestrial = FALSE, marine = TRUE, freshwater = FALSE)
# Extract present-day data sets
present = list_layers(datasets) %>% dplyr::select(dataset_code, layer_code, name, units, description, contains("cellsize"), version) %>% filter(version == 22) %>% filter(layer_code %in% c("BO22_damean", "BO22_parmean", "BO22_ph", "BO22_curvelmax_bdmean", "BO22_salinitymean_bdmean", "BO22_salinitymean_ss", "BO22_curvelmean_ss", "BO22_curvelmean_bdmean", "BO22_dissoxmean_bdmean", "BO22_lightbotmax_bdmean", "BO22_lightbotmean_bdmean", "BO22_nitratemean_bdmean", "BO22_tempmax_bdmean", "BO22_tempmean_bdmean", "BO22_tempmean_ss", "BO22_ppmean_ss", "BO22_dissoxmean_ss", "BO22_ppmean_bdmean", "BO22_chlomean_ss", "BO22_chlomean_bdmean"))
envVar = load_layers(present$layer_code)
symType = read.csv("../data/ITS2/148_20210301_DBV_20210401T112728.profiles.absolute.abund_CLEAN.csv", header = TRUE, check.names = FALSE)
symType = symType %>% mutate(sum = apply(symType[, c(2:(length(symType[1,])-1))], 1, function(x) {sum(x, na.rm = T)}))
symTypes = symType %>% dplyr::select(sample = Sample) %>% cbind(. ,(symType[, c(2:20)] / symType$sum))
head(symTypes)## sample A3-A3b-A3at-A3ax A3-A3at-A3b-A3q-A3s A3-A3s-A3q A3 A3-A3b-A3av-A3au-A3aw A4
## 1 SFK115 0 0.00000000 0 0 0 0
## 2 SFK022 0 0.00000000 0 0 0 0
## 3 SFK025 0 0.02520436 0 0 0 0
## 4 SFK095 0 0.00000000 0 0 0 0
## 5 SFK170 0 0.00000000 0 0 0 0
## 6 SFK175 0 0.00000000 0 0 0 0
## B18b B18c B5 C3/C3.10 C1/C3-C42.2-C1dl-C3gl-C3gm-C3gk C3-C1-C3.10 C3-C1dk-C15hx
## 1 0 0 0 1.0000000 0 0 0
## 2 0 0 0 1.0000000 0 0 0
## 3 0 0 0 0.9747956 0 0 0
## 4 0 0 0 1.0000000 0 0 0
## 5 0 0 0 1.0000000 0 0 0
## 6 0 0 0 1.0000000 0 0 0
## C3-C3go-C6c-C3gq-C3gp-C3gn-C3dw C16/C3-C16b C3-C3hb-C3ge-C3hc-C1dk
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## C3-C3gr-C3gt-C3gs-C3.10 C3/C1 C3
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0# check that all proportions add up to 1
apply(symTypes[, c(2:20)], 1, function(x) {
sum(x, na.rm = T)
})## [1] 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
## [10] 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
## [19] 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
## [28] 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
## [37] 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
## [46] 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
## [55] 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
## [64] 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
## [73] 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
## [82] 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
## [91] 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
## [100] 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
## [109] 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
## [118] 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
## [127] 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
## [136] 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
## [145] 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
## [154] 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
## [163] 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
## [172] 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
## [181] 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
## [190] 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
## [199] 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
## [208] 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
## [217] 1.000000 2.981776 1.000000symTypes = otuStack(symTypes, count.columns = c(2:length(symTypes[20, ])), condition.columns = c(1:1)) %>% filter(otu != "summ") %>% droplevels() %>% rename(sample = 3)
symTypes = symTypes %>% group_by(sample) %>% summarise(count = max(count)) %>% left_join(symTypes %>% dplyr::select(sample, otu, count)) %>% rename(sym = otu) %>% dplyr::select(-count)## Joining with `by = join_by(sample, count)`symTypes$sym = as.numeric(symTypes$sym)
envData = data.frame(popData, raster::extract(envVar, popData[,5:4])) %>% left_join(symTypes) %>% cbind(pcangsd[-131,c(6:8)])## Joining with `by = join_by(sample)`# See what data are highly colinear
corData = rcorr(as.matrix(envData[,c(7:ncol(envData))]), type = "pearson")
corDataFlat = melt(corData$r, value.name = "r")
pDataFlat = melt(corData$P, value.name = "p")
corDataBind = corDataFlat %>% left_join(pDataFlat, by = c("Var1","Var2"))
ggplot(corDataBind) +
geom_tile(aes(x = Var1, y = Var2, fill = r)) +
scale_fill_gradient2(low = "#3B9AB2FF", high = "#F21A00FF") +
geom_text(data = filter(corDataBind, r >= 0.7, p < 0.05),aes(x = Var1, y = Var2, label = round(r, 2))) +
geom_text(data = filter(corDataBind, r <= -0.7, p < 0.05),aes(x = Var1, y = Var2, label = round(r, 2))) +
theme_bw() +
theme(axis.text.x = element_text(angle = 90))
# Removing correlated data and biologically irrelevant measurements
envData2 = envData %>% dplyr::select("BO22_curvelmean_ss", "depthM", "BO22_lightbotmean_bdmean", "BO22_tempmean_bdmean", "BO22_ppmean_bdmean", "sym", "PC1", "PC2", "PC3")
# Checking again to make sure we've eliminated all colinearity
corData2 = cor(envData2)
corMelt2 = melt(corData2)
ggplot(corMelt2) +
geom_tile(aes(x = Var1, y = Var2, fill = value)) +
scale_fill_gradient2(low = "#3B9AB2FF", high = "#F21A00FF") +
geom_text(data = corMelt2[corMelt2$value >= 0.7,],aes(x = Var1, y = Var2, label = round(value, 2))) +
geom_text(data = corMelt2[corMelt2$value <= -0.7,],aes(x = Var1, y = Var2, label = round(value, 2))) +
theme_bw() +
theme(axis.text.x = element_text(angle = 90))
Looks good now!
Now we check variance inflation factors(VIF) to see if there is any multi-colinearity (VIF ≥ 10) between explanatory variables
vif = diag(solve(cor(envData2)))
vif## BO22_curvelmean_ss depthM BO22_lightbotmean_bdmean
## 2.132407 1.823732 3.924312
## BO22_tempmean_bdmean BO22_ppmean_bdmean sym
## 3.450330 3.478778 1.201146
## PC1 PC2 PC3
## 1.085288 1.176733 1.018379No issues with the VIF measures so we can continue to run the redundancy analysis
dbrda0 = dbrda(sintMa ~ 1, data = envData2)
dbrda1 = dbrda(sintMa ~ + depthM + BO22_curvelmean_ss + BO22_lightbotmean_bdmean + BO22_tempmean_bdmean + BO22_ppmean_bdmean + Condition(sym) + Condition(PC1 + PC2 + PC3), data = envData2)
# dbrda1 = dbrda(sintMa ~ BO22_curvelmean_ss + depthM + BO22_lightbotmean_bdmean + BO22_tempmean_bdmean + BO22_ppmean_bdmean + Condition(`A3-A3b-A3at-A3ax` + `A3-A3at-A3b-A3q-A3s` + `A3-A3s-A3q` + A3 + `A3-A3b-A3av-A3au-A3aw` + A4 + B18b + B5 + `C3/C3.10` + `C1/C3-C42.2-C1dl-C3gl-C3gm-C3gk` + `C3-C1-C3.10` + `C3-C1dk-C15hx` + `C3-C3go-C6c-C3gq-C3gp-C3gn-C3dw` + `C16/C3-C16b` + `C3-C3hb-C3ge-C3hc-C1dk` + `C3-C3gr-C3gt-C3gs-C3.10` + `C3/C1` + C3 + PC1 + PC2 + PC3 + PC4), data = envData2)
anova(dbrda1)## Permutation test for dbrda under reduced model
## Permutation: free
## Number of permutations: 999
##
## Model: dbrda(formula = sintMa ~ +depthM + BO22_curvelmean_ss + BO22_lightbotmean_bdmean + BO22_tempmean_bdmean + BO22_ppmean_bdmean + Condition(sym) + Condition(PC1 + PC2 + PC3), data = envData2)
## Df SumOfSqs F Pr(>F)
## Model 5 0.6329 1.7469 0.001 ***
## Residual 209 15.1447
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1RsquareAdj(dbrda1)## $r.squared
## [1] 0.0392843
##
## $adj.r.squared
## [1] 0.01711071summary(dbrda1)##
## Call:
## dbrda(formula = sintMa ~ +depthM + BO22_curvelmean_ss + BO22_lightbotmean_bdmean + BO22_tempmean_bdmean + BO22_ppmean_bdmean + Condition(sym) + Condition(PC1 + PC2 + PC3), data = envData2)
##
## Partitioning of squared Unknown distance:
## Inertia Proportion
## Total 16.1117 1.00000
## Conditioned 0.3341 0.02074
## Constrained 0.6329 0.03928
## Unconstrained 15.1447 0.93998
##
## Eigenvalues, and their contribution to the squared Unknown distance
## after removing the contribution of conditiniong variables
##
## Importance of components:
## dbRDA1 dbRDA2 dbRDA3 dbRDA4 dbRDA5 MDS1 MDS2 MDS3
## Eigenvalue 0.3472 0.089338 0.071386 0.065761 0.059286 1.13217 0.5365 0.43447
## Proportion Explained 0.0220 0.005662 0.004525 0.004168 0.003758 0.07176 0.0340 0.02754
## Cumulative Proportion NA NA NA NA NA NA NA NA
## MDS4 MDS5 MDS6 MDS7 MDS8 MDS9 MDS10
## Eigenvalue 0.23866 0.20365 0.16656 0.154994 0.145821 0.143957 0.136683
## Proportion Explained 0.01513 0.01291 0.01056 0.009824 0.009242 0.009124 0.008663
## Cumulative Proportion NA NA NA NA NA NA NA
## MDS11 MDS12 MDS13 MDS14 MDS15 MDS16 MDS17
## Eigenvalue 0.136035 0.133278 0.130692 0.130369 0.129329 0.127733 0.124623
## Proportion Explained 0.008622 0.008447 0.008283 0.008263 0.008197 0.008096 0.007899
## Cumulative Proportion NA NA NA NA NA NA NA
## MDS18 MDS19 MDS20 MDS21 MDS22 MDS23 MDS24
## Eigenvalue 0.123902 0.122050 0.120676 0.119665 0.119552 0.117597 0.116450
## Proportion Explained 0.007853 0.007736 0.007649 0.007584 0.007577 0.007453 0.007381
## Cumulative Proportion NA NA NA NA NA NA NA
## MDS25 MDS26 MDS27 MDS28 MDS29 MDS30 MDS31
## Eigenvalue 0.115028 0.113536 0.112712 0.112382 0.111062 0.110364 0.109783
## Proportion Explained 0.007291 0.007196 0.007144 0.007123 0.007039 0.006995 0.006958
## Cumulative Proportion NA NA NA NA NA NA NA
## MDS32 MDS33 MDS34 MDS35 MDS36 MDS37 MDS38
## Eigenvalue 0.1089 0.108623 0.107028 0.106477 0.105963 0.103989 0.103393
## Proportion Explained 0.0069 0.006885 0.006784 0.006749 0.006716 0.006591 0.006553
## Cumulative Proportion NA NA NA NA NA NA NA
## MDS39 MDS40 MDS41 MDS42 MDS43 MDS44 MDS45
## Eigenvalue 0.102453 0.101956 0.100612 0.100276 0.099940 0.098532 0.097567
## Proportion Explained 0.006494 0.006462 0.006377 0.006356 0.006334 0.006245 0.006184
## Cumulative Proportion NA NA NA NA NA NA NA
## MDS46 MDS47 MDS48 MDS49 MDS50 MDS51 MDS52
## Eigenvalue 0.097265 0.09625 0.095340 0.094438 0.094246 0.09404 0.093313
## Proportion Explained 0.006165 0.00610 0.006043 0.005986 0.005973 0.00596 0.005914
## Cumulative Proportion NA NA NA NA NA NA NA
## MDS53 MDS54 MDS55 MDS56 MDS57 MDS58 MDS59
## Eigenvalue 0.092239 0.091217 0.090532 0.090111 0.089627 0.088528 0.087811
## Proportion Explained 0.005846 0.005781 0.005738 0.005711 0.005681 0.005611 0.005566
## Cumulative Proportion NA NA NA NA NA NA NA
## MDS60 MDS61 MDS62 MDS63 MDS64 MDS65 MDS66
## Eigenvalue 0.087497 0.087016 0.086019 0.08583 0.085099 0.084665 0.083446
## Proportion Explained 0.005546 0.005515 0.005452 0.00544 0.005394 0.005366 0.005289
## Cumulative Proportion NA NA NA NA NA NA NA
## MDS67 MDS68 MDS69 MDS70 MDS71 MDS72 MDS73
## Eigenvalue 0.082733 0.082587 0.081325 0.080522 0.080041 0.079601 0.079123
## Proportion Explained 0.005244 0.005234 0.005154 0.005104 0.005073 0.005045 0.005015
## Cumulative Proportion NA NA NA NA NA NA NA
## MDS74 MDS75 MDS76 MDS77 MDS78 MDS79 MDS80
## Eigenvalue 0.078437 0.078193 0.077911 0.076857 0.075719 0.075360 0.074930
## Proportion Explained 0.004971 0.004956 0.004938 0.004871 0.004799 0.004776 0.004749
## Cumulative Proportion NA NA NA NA NA NA NA
## MDS81 MDS82 MDS83 MDS84 MDS85 MDS86 MDS87
## Eigenvalue 0.073663 0.073583 0.07320 0.072117 0.071391 0.070893 0.070479
## Proportion Explained 0.004669 0.004664 0.00464 0.004571 0.004525 0.004493 0.004467
## Cumulative Proportion NA NA NA NA NA NA NA
## MDS88 MDS89 MDS90 MDS91 MDS92 MDS93 MDS94
## Eigenvalue 0.070036 0.069639 0.069008 0.068262 0.067923 0.067656 0.067189
## Proportion Explained 0.004439 0.004414 0.004374 0.004327 0.004305 0.004288 0.004258
## Cumulative Proportion NA NA NA NA NA NA NA
## MDS95 MDS96 MDS97 MDS98 MDS99 MDS100 MDS101
## Eigenvalue 0.066717 0.066160 0.065838 0.06532 0.06468 0.064226 0.062833
## Proportion Explained 0.004229 0.004193 0.004173 0.00414 0.00410 0.004071 0.003982
## Cumulative Proportion NA NA NA NA NA NA NA
## MDS102 MDS103 MDS104 MDS105 MDS106 MDS107 MDS108
## Eigenvalue 0.062377 0.061830 0.061480 0.06106 0.060563 0.059595 0.059211
## Proportion Explained 0.003954 0.003919 0.003897 0.00387 0.003839 0.003777 0.003753
## Cumulative Proportion NA NA NA NA NA NA NA
## MDS109 MDS110 MDS111 MDS112 MDS113 MDS114 MDS115
## Eigenvalue 0.059040 0.058569 0.057573 0.057004 0.056860 0.056296 0.055666
## Proportion Explained 0.003742 0.003712 0.003649 0.003613 0.003604 0.003568 0.003528
## Cumulative Proportion NA NA NA NA NA NA NA
## MDS116 MDS117 MDS118 MDS119 MDS120 MDS121 MDS122
## Eigenvalue 0.05522 0.054880 0.054085 0.053303 0.052808 0.052191 0.051891
## Proportion Explained 0.00350 0.003478 0.003428 0.003378 0.003347 0.003308 0.003289
## Cumulative Proportion NA NA NA NA NA NA NA
## MDS123 MDS124 MDS125 MDS126 MDS127 MDS128 MDS129
## Eigenvalue 0.051415 0.05097 0.050679 0.050049 0.049275 0.048875 0.047835
## Proportion Explained 0.003259 0.00323 0.003212 0.003172 0.003123 0.003098 0.003032
## Cumulative Proportion NA NA NA NA NA NA NA
## MDS130 MDS131 MDS132 MDS133 MDS134 MDS135 MDS136
## Eigenvalue 0.047567 0.047128 0.046640 0.046206 0.045154 0.044480 0.044005
## Proportion Explained 0.003015 0.002987 0.002956 0.002929 0.002862 0.002819 0.002789
## Cumulative Proportion NA NA NA NA NA NA NA
## MDS137 MDS138 MDS139 MDS140 MDS141 MDS142 MDS143
## Eigenvalue 0.043769 0.043212 0.042364 0.041953 0.041248 0.040668 0.040566
## Proportion Explained 0.002774 0.002739 0.002685 0.002659 0.002614 0.002578 0.002571
## Cumulative Proportion NA NA NA NA NA NA NA
## MDS144 MDS145 MDS146 MDS147 MDS148 MDS149 MDS150
## Eigenvalue 0.040271 0.039644 0.039257 0.038613 0.037987 0.037653 0.037377
## Proportion Explained 0.002552 0.002513 0.002488 0.002447 0.002408 0.002386 0.002369
## Cumulative Proportion NA NA NA NA NA NA NA
## MDS151 MDS152 MDS153 MDS154 MDS155 MDS156 MDS157
## Eigenvalue 0.036558 0.036232 0.035646 0.035116 0.034859 0.034180 0.033893
## Proportion Explained 0.002317 0.002296 0.002259 0.002226 0.002209 0.002166 0.002148
## Cumulative Proportion NA NA NA NA NA NA NA
## MDS158 MDS159 MDS160 MDS161 MDS162 MDS163 MDS164
## Eigenvalue 0.033327 0.032593 0.032381 0.03171 0.031278 0.030993 0.029650
## Proportion Explained 0.002112 0.002066 0.002052 0.00201 0.001982 0.001964 0.001879
## Cumulative Proportion NA NA NA NA NA NA NA
## MDS165 MDS166 MDS167 MDS168 MDS169 MDS170 MDS171
## Eigenvalue 0.028960 0.028706 0.028262 0.027876 0.027420 0.026812 0.026649
## Proportion Explained 0.001835 0.001819 0.001791 0.001767 0.001738 0.001699 0.001689
## Cumulative Proportion NA NA NA NA NA NA NA
## MDS172 MDS173 MDS174 MDS175 MDS176 MDS177 MDS178
## Eigenvalue 0.025993 0.025172 0.024269 0.024099 0.023912 0.02224 0.021955
## Proportion Explained 0.001647 0.001595 0.001538 0.001527 0.001516 0.00141 0.001392
## Cumulative Proportion NA NA NA NA NA NA NA
## MDS179 MDS180 MDS181 MDS182 MDS183 MDS184 MDS185
## Eigenvalue 0.021407 0.020954 0.020397 0.020069 0.01972 0.019044 0.017646
## Proportion Explained 0.001357 0.001328 0.001293 0.001272 0.00125 0.001207 0.001118
## Cumulative Proportion NA NA NA NA NA NA NA
## MDS186 MDS187 MDS188 MDS189 MDS190 MDS191 MDS192
## Eigenvalue 0.016709 0.016501 0.016131 0.0157242 0.01483 0.0137934 0.013238
## Proportion Explained 0.001059 0.001046 0.001022 0.0009966 0.00094 0.0008742 0.000839
## Cumulative Proportion NA NA NA NA NA NA NA
## MDS193 MDS194 MDS195 MDS196 MDS197 MDS198
## Eigenvalue 0.0129572 0.0114694 0.0111595 0.010303 0.0100368 0.009971
## Proportion Explained 0.0008212 0.0007269 0.0007073 0.000653 0.0006361 0.000632
## Cumulative Proportion NA NA NA NA NA NA
## MDS199 MDS200 MDS201 MDS202 MDS203 iMDS1
## Eigenvalue 0.0080929 0.0071610 0.0058339 0.0046271 0.0025655 -0.0007415
## Proportion Explained 0.0005129 0.0004539 0.0003698 0.0002933 0.0001626 0.0000470
## Cumulative Proportion NA NA NA NA NA NA
## iMDS2 iMDS3 iMDS4 iMDS5 iMDS6
## Eigenvalue -0.0039731 -0.0078746 -0.026305 -0.056153 -0.082369
## Proportion Explained 0.0002518 0.0004991 0.001667 0.003559 0.005221
## Cumulative Proportion NA NA NA NA NA
##
## Accumulated constrained eigenvalues
## Importance of components:
## dbRDA1 dbRDA2 dbRDA3 dbRDA4 dbRDA5
## Eigenvalue 0.3472 0.08934 0.07139 0.06576 0.05929
## Proportion Explained 0.5485 0.14115 0.11279 0.10390 0.09367
## Cumulative Proportion 0.5485 0.68965 0.80243 0.90633 1.00000
##
## Scaling 2 for species and site scores
## * Species are scaled proportional to eigenvalues
## * Sites are unscaled: weighted dispersion equal on all dimensions
## * General scaling constant of scores: 7.698385
##
##
## Site scores (weighted sums of species scores)
##
## dbRDA1 dbRDA2 dbRDA3 dbRDA4 dbRDA5 MDS1
## 1 -0.958092 0.0282963 0.135254 -1.094064 -0.7040718 0.0539183
## 2 -0.189929 -1.4013993 0.254329 -0.317715 0.2249735 -0.0142703
## 3 -0.409004 -1.6017640 0.141864 -0.341630 -0.8989788 -0.0184982
## 4 -0.665664 -0.0180418 -0.605567 -0.760048 -0.1980064 0.0352832
## 5 -0.593225 -0.4057810 -0.307845 0.058549 -0.3191410 -0.0970398
## 6 -0.714939 -0.0293426 -0.319117 -0.458123 -0.6080646 -0.0434113
## 7 -0.097472 -1.4911936 0.245144 -0.148002 0.2159028 0.0076448
## 8 -0.077240 -1.5316728 -0.074734 -0.077088 -0.6685379 -0.0365348
## 9 -0.756609 0.1727393 0.077714 -0.926899 -0.0998855 0.0207745
## 10 0.184246 -0.7210670 -0.020671 -0.618763 -1.4221415 0.7881829
## 11 -0.519007 0.1147323 0.178037 -0.103376 -1.0699746 -0.0630512
## 12 -0.524515 -0.4674684 0.281386 -0.084086 0.0499855 0.1950999
## 13 -0.568336 -0.5384645 0.334818 -0.474620 -0.2699750 0.1600174
## 14 -0.529622 -0.1228112 0.174642 -0.395585 -0.4385787 0.1424753
## 15 -0.374114 -0.3843007 -0.266898 -1.081812 -0.1826775 0.0052114
## 16 -0.601878 0.0289132 0.250920 -0.313122 0.2256850 0.1025325
## 17 -0.669495 -0.4149300 -0.430739 0.067214 0.0449057 0.0947359
## 18 -0.292468 -0.1394168 -0.623618 -0.472874 0.1220129 0.0069961
## 19 -0.549377 -0.1679656 0.122769 -0.337018 0.1626708 0.0905625
## 20 -0.560393 -0.0004312 -0.339235 -0.277357 0.0848161 0.0662717
## 21 -0.404441 0.1490385 -0.281287 -0.161973 0.2492786 -0.0334475
## 22 -0.146232 -1.4510778 -0.055787 -0.536190 0.2963315 0.0205269
## 23 -0.634971 -0.5829102 0.119804 -0.298849 0.1504781 0.1253284
## 24 -0.565064 -0.1758305 0.028273 -0.989359 -0.5750723 0.0574187
## 25 -0.527696 0.2504819 -0.020750 -0.482907 -0.7271439 0.0662403
## 26 -0.477417 0.5774791 -0.320389 -0.981302 -0.2396549 0.5554301
## 27 1.209326 -0.8706270 0.254494 -1.213133 0.9432368 -0.1665357
## 28 1.362094 -1.1483596 0.036282 -1.025100 -0.3708283 -0.2471315
## 29 -0.530767 0.0173027 0.093015 -0.507598 -0.3061602 0.0746474
## 30 -0.423215 -0.5862369 0.231965 -0.045656 -0.3671395 0.0947337
## 31 -0.708786 -0.4876734 -0.085876 -0.409099 -0.0881512 0.2569152
## 32 -0.296126 -0.9521295 0.567446 -1.021254 -0.3149379 0.0871209
## 33 -0.519156 -0.4162350 0.371873 -1.299579 -1.4577116 -0.0503865
## 34 -0.530932 -0.6433283 -0.043663 -0.787066 -0.5833680 0.1147751
## 35 2.093101 1.2252598 -0.475655 -0.370135 -0.2088041 -0.9878631
## 36 2.137381 0.8405933 -0.188478 0.101753 -0.6373037 -1.1035998
## 37 2.004044 1.1473468 -0.168985 -0.546596 -1.1627570 -1.0740192
## 38 1.352943 -0.4233771 -0.440797 -2.017505 0.3716449 -0.2731243
## 39 1.279767 -1.6220176 0.543853 -1.154689 -0.2833538 -0.4185867
## 40 -0.402379 -0.3284630 -0.422010 0.311513 -0.3491938 0.3667999
## 41 0.001842 -1.3395638 0.693724 0.068341 0.7389011 0.4139066
## 42 1.192460 -1.2205653 0.151105 -0.475765 0.9590677 -0.3380569
## 43 -0.248581 -0.6223734 -0.093118 -0.270057 0.3540632 0.3984780
## 44 0.051513 -1.0224530 0.050861 -0.216902 0.4287194 0.4359347
## 45 1.368464 -0.7927421 0.085754 -1.303172 0.3556839 -0.5579218
## 46 -0.416946 -0.0615434 -0.142671 -0.494692 -0.4071488 0.4463323
## 47 1.338181 -1.2953385 0.439762 -0.777123 0.9329817 -0.3285473
## 48 0.082088 -1.2052326 0.063761 -0.282929 0.0486606 0.3972309
## 49 0.005711 -1.2105855 0.159553 -0.093373 0.2002883 0.5085030
## 50 0.628734 -1.1234784 -0.282374 -1.535752 0.3611495 -0.1296543
## 51 0.662322 -1.0895188 -0.040685 -0.327417 -0.5137929 -0.3254478
## 52 0.027586 -1.6834781 0.392997 0.222569 0.0106723 0.3125284
## 53 -0.042286 -1.0353069 -0.076041 -0.453493 0.0637228 0.4894294
## 54 0.132765 -1.0436498 -0.030497 -0.337366 -0.0627682 0.4520800
## 55 1.243236 -0.7637312 0.175608 -0.896416 0.3913693 -0.3978772
## 56 -0.551637 0.0618163 -0.375035 0.005840 0.4016451 0.0249840
## 57 -0.605099 -0.6788424 -0.078736 0.769568 0.0223534 0.0677022
## 58 -0.621454 -0.6319862 -0.195309 0.090707 -0.1963501 0.1112188
## 59 -0.655171 -0.4221642 -0.283994 0.358046 -0.0586426 0.1535037
## 60 -0.034234 -1.6652359 0.015867 0.537762 -0.1589041 0.1235329
## 61 -0.576534 -0.6751064 -0.238167 0.668851 -0.8903912 -0.0478118
## 62 2.017459 1.3973568 -0.567109 0.599263 -0.4734965 -1.1152300
## 63 -0.324763 0.3540563 -0.208637 0.406440 -0.9176961 0.3991233
## 64 2.088873 0.8211856 -0.797011 -0.002722 0.0469620 -1.1771015
## 65 -0.250605 0.4616084 -0.500598 -0.153151 -0.3897077 0.3871496
## 66 1.996148 0.8529758 -0.244349 0.416837 -0.0919431 -1.0985713
## 67 -0.583260 0.3707199 -0.759407 0.935600 0.2763962 0.5457172
## 68 -0.206924 0.1497721 -0.718411 -0.125876 -0.9821063 0.4178067
## 69 -0.111227 -0.7745797 -0.057931 -0.170006 -0.6438915 0.4882285
## 70 -0.373405 0.6678923 -1.106632 0.217170 -0.5105318 0.3377364
## 71 1.963797 0.6962202 -0.346981 0.299776 -0.3574861 -1.0525845
## 72 2.177980 0.3382953 -0.535465 0.763365 0.4868119 -1.3180021
## 73 -0.666956 -0.2556319 -0.768200 0.731735 1.0520752 0.3847154
## 74 -0.045661 -1.2927497 0.050534 0.906687 -0.0649490 0.3478351
## 75 1.222686 -2.2803470 -0.141374 -0.285968 0.2307576 -0.5964307
## 76 -0.245254 -1.4832115 0.387779 0.611112 0.1404870 0.5573625
## 77 -0.450142 0.2646452 -0.681773 0.356602 0.0246936 0.2963903
## 78 1.989246 0.2839925 -0.196521 0.667056 0.4209691 -1.2083557
## 79 1.281929 -1.7094552 -0.590324 -0.800235 0.5386098 -0.5066440
## 80 1.300340 0.5058559 -0.482290 1.303957 -0.1855409 -1.0770898
## 81 -0.578911 -0.7600305 -0.426256 0.252298 0.8293844 0.4535574
## 82 -0.057613 -1.5915910 -0.104044 0.776867 0.2282950 0.3478446
## 83 0.048270 -1.2203962 -0.113433 0.639420 0.1996132 0.2269844
## 84 -0.284947 -0.3178527 -0.582819 0.532445 -0.5260942 0.2462332
## 85 -0.658168 -1.8457763 -1.074475 1.341302 -0.3736645 0.3121944
## 86 -0.390418 -0.6632574 0.231323 1.039035 0.6238943 0.4333352
## 87 -0.480141 -0.4772757 -0.230029 0.314628 0.0142333 0.3579172
## 88 -0.366897 -0.4541809 0.041934 0.795778 -0.0900206 0.2863619
## 89 -0.428873 -0.0672562 -0.426162 0.389876 0.5431640 0.2824523
## 90 -0.505536 -0.0806299 -0.681398 0.248500 0.6500091 0.3435024
## 91 -0.519041 -0.3103439 -0.215931 0.702658 0.7528600 0.3395287
## 92 -0.613756 -0.5408061 -0.291840 -0.102884 0.4419416 0.0093066
## 93 -0.087765 -1.9135043 0.020296 0.775773 0.1403660 0.0489276
## 94 -0.232633 -1.5286149 0.102974 0.827350 -0.2688504 0.1187442
## 95 2.310608 0.6275199 -0.362713 0.700998 -0.0425590 -1.5333630
## 96 -0.165591 -1.3801314 0.112921 0.705202 -0.0822476 0.0810878
## 97 -0.631944 -1.1680879 -0.347091 1.654916 -0.7273523 0.2118162
## 98 -0.599755 -0.3356091 -0.934013 0.133741 -0.5055278 0.0294334
## 99 2.062424 0.5796733 -0.749233 1.475640 -0.0928271 -1.5378200
## 100 -0.672106 -0.8110040 -1.029495 0.528915 0.0008569 0.1964618
## 101 -0.728405 0.6117586 0.126589 -0.412450 0.4962948 -0.0482401
## 102 -0.622490 0.6691061 -0.559305 -1.379163 0.9921039 -0.0713981
## 103 -0.494061 0.6700189 -0.256563 -0.745768 0.7701545 -0.0524947
## 104 -0.831005 0.3563160 -0.447588 -0.373706 0.6923856 0.1581004
## 105 -0.482654 0.6074312 -0.468356 -0.242168 0.6895894 -0.0732986
## 106 -0.661026 0.8508348 -0.093309 -0.732291 0.0776106 0.0048946
## 107 -0.613142 0.2991404 0.244322 -0.503050 0.4634309 0.0523044
## 108 -0.524356 0.3513916 -0.279959 -0.677823 0.4115385 -0.0794864
## 109 -0.483426 0.4939469 -0.158015 -0.304297 0.8699131 -0.0222836
## 110 -0.651194 0.3571680 0.213708 -0.129656 0.2122619 0.0562494
## 111 -0.598606 0.6601949 -0.441575 -0.489530 0.5289278 0.0326903
## 112 -0.518466 0.2001003 -0.373822 -0.298191 0.2303503 -0.0374668
## 113 -0.483058 0.4292687 -0.058230 -0.208731 0.3439509 -0.0561353
## 114 -0.686464 0.5943296 -0.413346 -0.804803 0.6969381 0.0593755
## 115 -0.782638 -0.0202665 -0.253278 -0.551012 0.8012648 0.0487897
## 116 -0.506673 0.4379316 -0.330898 -0.491857 0.4882212 0.0103063
## 117 -0.740987 -0.2079897 0.165297 -0.705695 1.0704987 0.1623066
## 118 -0.596835 0.5702444 -0.473672 -0.724764 0.9654928 0.0882793
## 119 -0.498793 0.2692308 -0.305809 -0.589233 0.5339630 0.0310829
## 120 -0.165169 0.7516254 0.193959 -0.698138 0.4397509 0.0329277
## 121 -0.727533 0.5934914 0.023389 -0.496501 0.5262761 0.1208888
## 122 -0.610815 0.3025016 -0.425205 -0.430005 1.7535256 0.1509402
## 123 -0.692408 0.6216373 -0.034842 -0.736440 0.7957061 0.1308796
## 124 -0.633859 0.2902985 -0.145818 -0.288265 1.4210161 0.0465747
## 125 -0.594930 0.3957057 0.221401 -0.847367 0.2308287 0.0218218
## 126 -0.971204 0.4894040 -0.026163 -0.178798 -0.1527787 0.0913330
## 127 -0.822900 0.5247036 -0.326726 -0.668734 0.1187894 0.0701855
## 128 -0.561266 0.4453228 -0.033569 -0.178786 1.0543294 0.4488227
## 129 -0.439954 0.6211536 -0.176561 -0.111666 0.1999472 0.3963692
## 130 -0.138979 -0.7793800 0.016116 0.816266 0.4689073 0.5226288
## 132 2.056372 1.1422465 -0.135156 0.765991 0.3698284 -1.1240865
## 133 0.654873 -0.5135329 0.179191 -1.114600 1.1142707 -0.0636149
## 134 0.215998 -1.4563338 0.338931 0.389654 1.4787820 0.5265838
## 135 -0.452837 0.1827560 -0.049083 0.169756 0.5801969 0.4443321
## 136 -0.381488 0.1601039 0.298174 0.550960 0.2188644 0.3900800
## 137 -0.796048 -0.2581890 0.780022 0.618169 0.8001680 0.8752436
## 138 -0.465395 0.2713363 0.265163 -0.547561 0.6312787 0.4800753
## 139 -0.373715 0.7078792 -0.056828 0.522446 1.3763081 0.4690289
## 140 -0.331106 0.7787593 -0.121280 -0.249932 0.6120459 0.3368191
## 141 2.066178 1.6455727 -0.192520 -0.059376 0.5380941 -1.2528258
## 142 2.202566 1.4048547 -0.469314 0.285962 0.2803642 -1.2677078
## 143 1.564889 0.8147619 -0.622053 0.993454 -0.4592405 -0.9058399
## 144 1.583441 1.3243571 -0.180755 0.857001 -0.2843578 -0.9612884
## 145 2.121366 1.5973627 0.195130 0.412698 0.7770012 -1.2006660
## 146 -0.158625 -0.8424404 0.125395 0.062472 0.4554021 0.5480511
## 147 -1.040501 0.7675997 0.100034 0.173925 0.8519423 0.7273726
## 148 2.100042 1.3068906 0.094051 -0.121465 0.5766700 -1.0901295
## 149 2.141745 0.7748737 -0.028607 0.338963 -0.1363600 -1.1648683
## 150 0.763622 -0.2398985 0.849938 -1.326033 1.1510280 -0.1112568
## 151 1.126839 -0.2985289 0.546794 -1.268548 1.5192720 -0.1572580
## 152 -0.980472 0.6545323 -0.494015 0.298117 0.2752153 0.5252666
## 153 2.084295 0.8916131 -0.253642 0.060665 0.1247914 -1.1184988
## 154 -0.890873 0.2683494 -0.392815 -0.625126 1.2240866 0.1261773
## 155 -1.182750 1.3653633 -0.499002 -0.058233 -0.3157744 0.1684962
## 156 -0.988048 -0.5990873 0.109260 -0.616131 -0.3166994 0.3336867
## 157 -0.450556 -0.2246822 0.325015 0.376952 0.7418578 0.4866559
## 158 -0.144453 -0.8397573 0.571193 -0.344653 1.4667346 0.7199798
## 159 -0.465061 0.0507013 -0.054130 0.194644 0.9700448 0.4237042
## 160 -0.766089 -0.4212740 0.550507 0.389213 -0.0689478 0.8398935
## 161 -0.331085 -0.1453427 -0.699814 -0.008546 -0.5756906 0.5182059
## 162 -0.291685 0.1644390 -0.460444 -0.413143 -1.5696093 0.4912875
## 163 2.170405 0.8314463 -0.001814 -0.441407 -0.5084539 -0.9351448
## 164 0.135604 -1.4796039 0.364684 -0.078711 -1.3967910 0.5870841
## 165 2.279455 1.4577452 -0.159296 -0.414322 -0.3389876 -0.9339759
## 166 2.245815 1.2750091 -0.052354 -0.553084 -1.0248791 -0.8828733
## 167 -0.395679 0.1022662 -0.072744 -0.108073 -1.3254898 0.5918133
## 168 -0.343048 0.5689776 0.489607 -0.995353 -1.3983387 0.6178046
## 169 -0.650274 1.3205382 -2.540393 1.702016 -0.2856397 0.2231687
## 170 -0.370119 1.1781166 -2.661244 1.028048 -0.3769037 0.1078557
## 171 -0.471095 0.9181225 -2.414583 1.557286 -0.3365751 0.2370105
## 172 -0.562942 1.0927425 -2.629605 1.093270 -0.1008366 0.2901946
## 173 -0.520079 0.0811879 -0.231053 -0.542944 -0.6411243 0.7817843
## 174 -0.091664 -0.6972292 0.128768 -1.593068 -0.3852385 0.9048440
## 175 -0.468496 0.3619587 -0.108275 -1.019317 -0.8678606 0.7669229
## 176 2.141788 1.3951965 -0.129766 -0.588584 -1.2668901 -0.8757004
## 177 -0.505369 0.7505751 -0.243031 -0.849841 -0.9015673 0.7308182
## 178 2.201976 1.2782898 0.259680 1.083171 -2.0488059 -0.8639895
## 179 2.185535 1.1157527 -0.733278 -0.729167 -0.8667293 -0.7315716
## 180 -0.696632 0.3569771 0.692983 0.674160 0.0431219 0.0540893
## 181 -0.617922 0.5827354 0.692056 0.406098 -0.1610349 -0.0476748
## 182 -0.571513 0.4512917 0.451266 0.628692 -0.1391120 -0.0956663
## 183 2.171292 1.0674869 0.837738 1.522925 -0.0928767 -1.0254803
## 184 -0.039154 -0.9854798 1.472425 0.929516 0.4032605 0.7126631
## 185 0.014010 -1.5047979 0.675158 1.166958 0.0537033 0.6261817
## 186 1.930092 1.2698966 0.780327 0.913292 -0.2406041 -0.9179649
## 187 -0.586363 0.5694148 0.766271 0.777369 0.1068661 0.5880553
## 188 -0.504434 0.9854630 1.588929 0.003891 0.2530023 0.6570990
## 189 2.017411 0.9609907 -0.035749 0.794698 0.3548855 -0.9868713
## 190 -0.507218 0.6460969 0.877312 0.080105 0.1413924 0.4960420
## 191 -0.040816 -1.3969076 1.203114 1.147266 -0.4529091 0.5142399
## 192 0.201697 0.6719876 1.093030 1.058243 0.4020731 0.2745040
## 193 -0.473234 0.8829355 0.846977 0.691294 0.8188920 0.5414325
## 194 -0.829135 0.1406403 1.097989 0.706329 -0.6612152 0.1604306
## 195 -0.625783 1.2351845 0.708505 -0.082058 -0.5629695 -0.1547960
## 196 -0.514659 0.7006030 0.062995 0.504514 -0.4895330 -0.2418915
## 197 -0.550574 0.1900873 0.226910 0.499347 -0.6979439 -0.0513287
## 198 -0.938203 0.2779070 0.998923 0.515973 0.0033094 -0.0077506
## 199 -0.647968 0.1668820 0.713711 0.382500 -0.2259156 0.0002466
## 200 -0.397673 1.3855278 0.108594 0.318759 -0.6628792 -0.1992138
## 201 -0.230006 -0.8633553 1.107223 0.624352 -0.9992816 -0.0234701
## 202 -0.572105 0.4685940 0.432170 -0.107308 -0.1301141 -0.0363704
## 203 -0.490485 0.2974118 0.517801 0.599826 -1.0825100 -0.2248306
## 204 -0.690984 0.0577895 0.442175 0.308468 -0.6128527 -0.0253304
## 205 -0.609871 0.2117476 0.468253 0.382039 -0.2387947 -0.0728607
## 206 -0.512814 0.8385612 0.716088 0.053365 -0.3970998 -0.0701689
## 207 -0.544435 0.4835073 0.364004 -0.164850 -0.8560329 -0.1634384
## 208 -0.649170 0.5144617 1.109394 -0.047505 0.4987412 -0.1419400
## 209 -0.241990 -0.3569793 0.177618 0.182606 0.3913970 -0.0497915
## 210 -0.695941 0.2989461 0.647431 0.284790 -0.8048091 -0.1040708
## 211 -0.663510 0.2190971 0.926076 0.132262 -0.1378519 -0.0043869
## 212 -0.583117 1.2174736 0.935023 0.277021 -1.3703462 -0.0813187
## 213 -0.260372 -0.5631690 1.704310 0.782938 -0.5499994 0.0477053
## 214 -0.731616 0.1004971 0.764989 0.462869 -0.3763565 0.0812733
## 215 -0.574167 1.1368202 0.043447 0.266202 -0.5631148 -0.0399757
## 216 -0.374368 -0.6260897 1.103084 0.854488 -1.0711013 -0.0073943
## 217 -0.603033 0.4446625 0.927840 0.462781 0.0443342 0.5780239
## 218 2.056833 1.6272676 0.834680 0.513827 -0.6884711 -1.0709812
## 219 -0.590164 0.2795861 0.686667 0.431109 0.2166065 0.5647443
## 220 -0.256623 0.0990163 1.205480 0.909677 0.1190216 0.5835910
##
##
## Site constraints (linear combinations of constraining variables)
##
## dbRDA1 dbRDA2 dbRDA3 dbRDA4 dbRDA5 MDS1
## 1 -0.63524 -0.4934 -0.06028263 -0.5491387 -0.2443752 0.0539183
## 2 -0.56799 -0.4819 -0.04607788 -0.5535456 -0.1760692 -0.0142703
## 3 -0.62013 -0.4801 -0.05752094 -0.5713032 -0.2079568 -0.0184982
## 4 -0.51668 -0.4960 -0.03848242 -0.5148231 -0.1816979 0.0352832
## 5 -0.59693 -0.5030 -0.06028334 -0.5236111 -0.2574377 -0.0970398
## 6 -0.69172 -0.5768 -0.08775366 -0.4046790 -0.5033842 -0.0434113
## 7 -0.59179 -0.4819 -0.05027595 -0.5590322 -0.1932317 0.0076448
## 8 -0.58567 -0.5039 -0.05686573 -0.5106055 -0.2763645 -0.0365348
## 9 -0.60308 -0.5039 -0.05944262 -0.5180343 -0.2729208 0.0207745
## 10 1.04306 0.2665 -0.41268700 -1.2617530 -0.8870629 0.7881829
## 11 -0.49070 -0.6171 -0.06135129 -0.2690133 -0.5509137 -0.0630512
## 12 -0.25681 -0.5278 0.01831556 -0.3888152 -0.1094606 0.1950999
## 13 -0.20841 -0.5256 0.02384645 -0.3828811 -0.0761863 0.1600174
## 14 -0.29422 -0.5206 0.00609335 -0.4130868 -0.1127747 0.1424753
## 15 -0.24826 -0.4743 0.02402919 -0.4964238 0.0553234 0.0052114
## 16 -0.25766 -0.5083 0.01305038 -0.4280893 -0.0671064 0.1025325
## 17 -0.31628 -0.5173 -0.00217491 -0.4261623 -0.1292519 0.0947359
## 18 -0.30562 -0.4990 0.01142283 -0.4597657 -0.0471206 0.0069961
## 19 -0.28493 -0.4961 0.01105411 -0.4592492 -0.0440237 0.0905625
## 20 -0.32863 -0.4969 -0.02021749 -0.4463262 -0.1663639 0.0662717
## 21 -0.31837 -0.5231 0.00367964 -0.4154721 -0.1310803 -0.0334475
## 22 -0.35835 -0.5196 -0.00635882 -0.4344151 -0.1429087 0.0205269
## 23 -0.36234 -0.5217 -0.00561700 -0.4304038 -0.1554951 0.1253284
## 24 -0.34268 -0.5217 -0.00064761 -0.4208845 -0.1471264 0.0574187
## 25 -0.31215 -0.5169 -0.00149104 -0.4280047 -0.1158781 0.0662403
## 26 0.55105 0.3671 -0.13707840 -0.5410884 -0.1097624 0.5554301
## 27 0.62834 0.3781 -0.10743535 -0.5492366 0.0053908 -0.1665357
## 28 0.62866 0.3852 -0.11539398 -0.5619791 -0.0002024 -0.2471315
## 29 -0.29627 -0.5206 0.00599765 -0.4118180 -0.1182391 0.0746474
## 30 -0.32483 -0.4988 -0.05351206 -0.4838162 -0.3026673 0.0947337
## 31 -0.21393 -0.5027 -0.03335401 -0.4518027 -0.2481103 0.2569152
## 32 -0.31110 -0.2494 -0.20532246 -0.7702347 -0.1339480 0.0871209
## 33 -0.55622 -0.5132 -0.03861105 -0.3312192 -0.8709895 -0.0503865
## 34 -0.29155 -0.5046 -0.04820973 -0.4672793 -0.3253319 0.1147751
## 35 0.64297 0.4081 -0.10614223 -0.6049870 0.0893366 -0.9878631
## 36 0.43217 0.2513 -0.18906432 -0.3486342 -0.5309144 -1.1035998
## 37 0.36289 0.2755 -0.25688877 -0.4363784 -0.7267333 -1.0740192
## 38 0.62910 0.3850 -0.11547467 -0.5623953 0.0003614 -0.2731243
## 39 0.26116 -0.6636 0.10148319 0.0009564 -0.1967889 -0.4185867
## 40 0.28855 -0.6576 0.10238700 -0.0014177 -0.1665915 0.3667999
## 41 0.42888 -0.5493 0.16160878 -0.1816810 0.2582620 0.4139066
## 42 0.46301 -0.5244 0.16991656 -0.2243850 0.3452163 -0.3380569
## 43 0.40331 -0.5237 0.09860360 -0.2612886 0.0801781 0.3984780
## 44 0.52884 -0.2527 -0.02670023 -0.5540140 0.3559026 0.4359347
## 45 0.30334 -0.3872 -0.11620406 -0.3450971 -0.2001922 -0.5579218
## 46 0.49568 -0.6491 0.09269456 0.0113337 -0.2479797 0.4463323
## 47 0.50134 -0.5245 0.17770348 -0.2079269 0.3535956 -0.3285473
## 48 0.42896 -0.4036 -0.08251409 -0.2863652 -0.1532384 0.3972309
## 49 0.64441 -0.2744 -0.00181107 -0.4862905 0.3800209 0.5085030
## 50 0.24473 -0.5326 0.13377237 -0.0969937 -0.4387219 -0.1296543
## 51 0.02692 -0.6685 0.05900716 0.1139970 -0.9763032 -0.3254478
## 52 0.26895 -0.6579 0.09892542 -0.0086692 -0.1716617 0.3125284
## 53 0.57888 -0.2664 -0.01985485 -0.5155828 0.3460181 0.4894294
## 54 0.60339 -0.2725 -0.00987070 -0.5014082 0.3674332 0.4520800
## 55 0.46991 -0.4055 -0.07411978 -0.2706858 -0.1400328 -0.3978772
## 56 -0.28239 -0.5085 -0.48545850 0.0606531 0.2255000 0.0249840
## 57 -0.34912 -0.8456 -0.29733229 0.5197827 0.0197029 0.0677022
## 58 -0.29694 -0.8119 -0.34228070 0.4449507 -0.0968054 0.1112188
## 59 -0.20348 -0.8393 -0.26550358 0.5453362 0.1230431 0.1535037
## 60 -0.20120 -0.8287 -0.32355725 0.5011482 -0.0746135 0.1235329
## 61 -0.62053 -0.8253 -0.35967030 0.4195148 -0.1203076 -0.0478118
## 62 0.38127 0.1873 -0.44254184 0.1842747 -0.2957415 -1.1152300
## 63 0.33717 0.1675 -0.45863288 0.2061793 -0.3631496 0.3991233
## 64 0.38509 0.1836 -0.43885286 0.1874734 -0.2838684 -1.1771015
## 65 0.33503 0.1758 -0.51634336 0.1657078 -0.5759025 0.3871496
## 66 0.38598 0.1888 -0.44215918 0.1815114 -0.2796088 -1.0985713
## 67 0.41097 0.1878 -0.43916957 0.1844034 -0.2645643 0.5457172
## 68 0.33448 0.1769 -0.51725785 0.1646312 -0.5774763 0.4178067
## 69 0.35607 0.1766 -0.51192332 0.1733880 -0.5637129 0.4882285
## 70 0.34055 0.3277 -0.68839195 0.0743787 -0.7174154 0.3377364
## 71 0.46423 0.2054 -0.48078905 0.1455334 -0.4209441 -1.0525845
## 72 0.26834 -0.7831 -0.20997141 0.5254564 0.3476506 -1.3180021
## 73 0.19731 -0.8764 -0.18202065 0.7133561 0.2675528 0.3847154
## 74 0.28397 -0.5710 -0.37802158 0.3224143 0.3842157 0.3478351
## 75 0.05877 -0.9829 -0.24117354 0.8923640 -0.1487131 -0.5964307
## 76 0.31632 -0.8109 -0.13943875 0.6167037 0.5433739 0.5573625
## 77 0.26053 -0.5719 -0.38149849 0.3178346 0.3680506 0.2963903
## 78 0.24900 -0.8344 -0.17064910 0.6470080 0.4059216 -1.2083557
## 79 0.26571 -0.5888 -0.37882844 0.3500144 0.3354410 -0.5066440
## 80 -0.00298 -0.8497 -0.20604579 0.7681608 -0.3670697 -1.0770898
## 81 0.27245 -0.7796 -0.21010633 0.5221928 0.3650440 0.4535574
## 82 0.18001 -0.8674 -0.19181778 0.6943084 0.2710980 0.3478446
## 83 0.18692 -0.6093 -0.40030656 0.3721828 0.2184549 0.2269844
## 84 0.18637 -0.6117 -0.39826504 0.3758200 0.2173339 0.2462332
## 85 0.21997 -0.8751 -0.17857989 0.7183895 0.2825038 0.3121944
## 86 0.31573 -0.8020 -0.14781273 0.6038292 0.5382539 0.4333352
## 87 0.19416 -0.8480 -0.24385392 0.6354120 0.1009927 0.3579172
## 88 0.26836 -0.8760 -0.16906841 0.7305728 0.3197321 0.2863619
## 89 0.23743 -0.5739 -0.38313301 0.3158818 0.3553921 0.2824523
## 90 0.30562 -0.5438 -0.35859180 0.2744275 0.4911935 0.3435024
## 91 0.19259 -0.8709 -0.18961655 0.7047358 0.2467896 0.3395287
## 92 -0.49490 -0.7999 -0.38303276 0.3738962 -0.1735451 0.0093066
## 93 -0.39460 -0.8403 -0.30833067 0.5030523 -0.0049017 0.0489276
## 94 -0.26141 -0.8489 -0.27864668 0.5499820 0.0670331 0.1187442
## 95 -0.14947 -0.8694 -0.25659953 0.6158330 0.0766759 -1.5333630
## 96 -0.29940 -0.8562 -0.29280060 0.5547247 0.0092643 0.0810878
## 97 -0.58898 -0.8527 -0.33844858 0.6389108 -0.7754391 0.2118162
## 98 -0.43164 -0.6948 -0.55864341 0.3835224 -0.4181953 0.0294334
## 99 -0.17369 -0.8154 -0.25463645 0.5095732 0.2034824 -1.5378200
## 100 -0.18632 -0.8298 -0.32254321 0.5080320 -0.0911198 0.1964618
## 101 -0.69154 0.4138 -0.19389551 -0.6423244 0.4544509 -0.0482401
## 102 -0.67627 0.3664 -0.14838302 -0.5298781 0.5157452 -0.0713981
## 103 -0.61035 0.3638 -0.11344423 -0.5061802 0.6242461 -0.0524947
## 104 -0.55393 0.3801 -0.10646838 -0.5195751 0.6740255 0.1581004
## 105 -0.51685 0.3541 -0.10397028 -0.4629184 0.6309226 -0.0732986
## 106 -0.50244 0.3540 -0.10089658 -0.4526516 0.6206843 0.0048946
## 107 -0.54305 0.3802 -0.16723251 -0.5425215 0.4517838 0.0523044
## 108 -0.56998 0.3767 -0.16856465 -0.5412739 0.4269021 -0.0794864
## 109 -0.50638 0.3820 -0.10075085 -0.5127575 0.7051616 -0.0222836
## 110 -0.52034 0.3530 -0.10213714 -0.4576637 0.6246107 0.0562494
## 111 -0.52160 0.3506 -0.10174076 -0.4560380 0.6174290 0.0326903
## 112 -0.59338 0.3795 -0.17601080 -0.5521409 0.4079333 -0.0374668
## 113 -0.59352 0.3779 -0.17390050 -0.5487379 0.4098925 -0.0561353
## 114 -0.54497 0.3890 -0.10001480 -0.5316154 0.6954813 0.0593755
## 115 -0.58595 0.3589 -0.11963305 -0.4859376 0.5875352 0.0487897
## 116 -0.53113 0.3826 -0.10434905 -0.5167086 0.6882481 0.0103063
## 117 -0.46678 0.3762 -0.08934115 -0.4913138 0.7146768 0.1623066
## 118 -0.44537 0.3739 -0.08248553 -0.4800018 0.7278856 0.0882793
## 119 -0.45987 0.3487 -0.08997787 -0.4340413 0.6377728 0.0310829
## 120 -0.40607 0.3714 -0.07355976 -0.4623327 0.7369059 0.0329277
## 121 -0.42318 0.3741 -0.07900487 -0.4744981 0.7392268 0.1208888
## 122 -0.46474 0.3783 -0.08911868 -0.4925146 0.7247661 0.1509402
## 123 -0.46664 0.3764 -0.08673639 -0.4874704 0.7221992 0.1308796
## 124 -0.49048 0.3728 -0.08836553 -0.4888023 0.7043214 0.0465747
## 125 -0.53786 0.3857 -0.09467656 -0.5282120 0.7208874 0.0218218
## 126 -0.66685 0.3208 -0.11509106 -0.3741550 0.1472574 0.0913330
## 127 -0.52714 0.3754 -0.15978788 -0.5263474 0.4470207 0.0701855
## 128 0.35696 0.1938 0.05835095 0.0671613 0.6790565 0.4488227
## 129 0.36357 0.2000 0.05590987 0.0585031 0.7023375 0.3963692
## 130 0.35818 0.2226 -0.00003005 -0.0085747 0.5334181 0.5226288
## 132 0.41119 0.1999 0.06356816 0.0683470 0.7345542 -1.1240865
## 133 0.32784 0.3244 0.09002245 -0.0384778 0.4569765 -0.0636149
## 134 0.42165 0.2057 0.07205467 0.0585535 0.7586365 0.5265838
## 135 0.41187 0.1987 0.06559147 0.0710324 0.7389910 0.4443321
## 136 0.40955 0.1970 0.06441586 0.0709524 0.7265590 0.3900800
## 137 0.38826 0.3518 0.11504704 -0.0736557 0.5684225 0.8752436
## 138 0.38134 0.1924 0.06366246 0.0740312 0.6975534 0.4800753
## 139 0.38877 0.1989 0.06088451 0.0642774 0.7227237 0.4690289
## 140 0.33188 0.4604 -0.16213848 -0.2827219 0.6404552 0.3368191
## 141 0.37791 0.4648 -0.15749331 -0.2766340 0.6698367 -1.2528258
## 142 0.37766 0.4625 -0.15598473 -0.2740230 0.6683204 -1.2677078
## 143 0.38153 0.3605 0.10747797 -0.0965951 0.6043477 -0.9058399
## 144 0.21756 0.2349 0.04509500 0.1113724 0.1117213 -0.9612884
## 145 0.45267 0.1942 0.07504545 0.0885892 0.7491823 -1.2006660
## 146 0.40315 0.2196 0.00782823 0.0035905 0.5532760 0.5480511
## 147 0.37939 0.3569 0.11045477 -0.0903694 0.5991833 0.7273726
## 148 0.43029 0.1976 0.06806774 0.0776880 0.7354471 -1.0901295
## 149 0.35843 0.2232 -0.00190638 -0.0116316 0.5303017 -1.1648683
## 150 0.42858 0.3592 0.11584695 -0.0852008 0.6344079 -0.1112568
## 151 0.67016 0.3618 0.16123082 -0.1832122 1.3807221 -0.1572580
## 152 0.09075 0.1934 0.00810888 0.1591185 -0.0837473 0.5252666
## 153 0.46726 0.3143 0.04133101 -0.1631489 0.8707812 -1.1184988
## 154 -0.52425 0.3757 -0.16096584 -0.5301527 0.4521482 0.1261773
## 155 -0.75298 0.3449 -0.14247553 -0.3422752 -0.1519807 0.1684962
## 156 -0.70315 0.3798 -0.12484787 -0.3973880 -0.0165294 0.3336867
## 157 0.29844 0.2259 -0.01326760 -0.0342668 0.5171957 0.4866559
## 158 0.58194 0.3881 0.08684670 -0.2835089 1.1783025 0.7199798
## 159 0.27643 0.2272 -0.01820654 -0.0422012 0.5053990 0.4237042
## 160 0.37593 0.3547 0.11065734 -0.0875351 0.5863825 0.8398935
## 161 0.58176 0.2647 -0.11574326 -0.6298547 -1.1885344 0.5182059
## 162 0.66692 0.2632 -0.10025539 -0.6028618 -1.1525398 0.4912875
## 163 0.88557 0.3117 -0.03986374 -0.6421959 -0.8657638 -0.9351448
## 164 0.74332 0.1703 -0.11342082 -0.4039138 -1.4007078 0.5870841
## 165 0.94824 0.3088 -0.02911489 -0.6223203 -0.8477694 -0.9339759
## 166 0.94384 0.3058 -0.02713934 -0.6160966 -0.8599786 -0.8828733
## 167 0.63573 0.1894 -0.12198436 -0.4672983 -1.4036911 0.5918133
## 168 0.76373 0.2801 -0.07495488 -0.6107247 -1.0599116 0.6178046
## 169 -0.27704 1.5151 -2.64803806 1.5348601 -0.3480006 0.2231687
## 170 -0.27329 1.5192 -2.65097499 1.5275515 -0.3376124 0.1078557
## 171 -0.25636 1.5288 -2.64189621 1.5139508 -0.2874740 0.2370105
## 172 -0.29694 1.5307 -2.64950054 1.4992170 -0.2977527 0.2901946
## 173 0.99188 0.3542 -0.00789814 -0.6990131 -0.6791011 0.7817843
## 174 1.01616 0.3107 -0.01919713 -0.6076462 -0.8084664 0.9048440
## 175 0.86220 0.2731 -0.11326650 -0.5974164 -1.2333812 0.7669229
## 176 0.90306 0.3104 -0.03821229 -0.6361374 -0.8741995 -0.8757004
## 177 0.95451 0.2487 -0.04081656 -0.5029892 -1.0217047 0.7308182
## 178 1.01101 0.3036 -0.01399270 -0.5957388 -0.8227386 -0.8639895
## 179 1.03582 0.3290 -0.06601442 -0.6623473 -0.9529923 -0.7315716
## 180 -0.53897 0.3328 0.74290651 0.5329582 -0.3952038 0.0540893
## 181 -0.53598 0.3349 0.74267217 0.5304012 -0.3837777 -0.0476748
## 182 -0.67303 0.3692 0.71650825 0.4285943 -0.3671464 -0.0956663
## 183 0.54033 0.3639 0.92205260 0.7130098 0.1392670 -1.0254803
## 184 0.55900 0.3045 0.97284574 0.8573389 0.1985471 0.7126631
## 185 0.61086 0.2745 0.98080754 0.9278814 0.1443735 0.6261817
## 186 0.76722 0.3353 1.00256736 0.8358545 0.3237594 -0.9179649
## 187 0.56814 0.2753 0.97184890 0.9126374 0.1228861 0.5880553
## 188 0.57449 0.2787 0.97135776 0.9072854 0.1450503 0.6570990
## 189 0.58617 0.3389 0.98624268 0.7959299 0.3186234 -0.9868713
## 190 0.52716 0.2811 0.96064934 0.8924676 0.1103479 0.4960420
## 191 0.43666 0.2834 0.94435734 0.8678832 0.0602220 0.5142399
## 192 0.48115 0.2841 0.95206742 0.8799215 0.0859001 0.2745040
## 193 0.49421 0.5411 0.74518208 0.5560557 0.0511396 0.5414325
## 194 -0.71803 0.3808 0.64851918 0.3708105 -0.6001592 0.1604306
## 195 -0.80678 0.6101 0.47423742 0.1058311 -0.5559662 -0.1547960
## 196 -0.78712 0.6226 0.47897465 0.0835721 -0.5042432 -0.2418915
## 197 -0.67102 0.3533 0.71700233 0.4595968 -0.4116724 -0.0513287
## 198 -0.77189 0.3463 0.69310477 0.4462992 -0.4905532 -0.0077506
## 199 -0.71601 0.3689 0.71066216 0.4183204 -0.3834558 0.0002466
## 200 -0.74272 0.6227 0.48635835 0.0952086 -0.4806928 -0.1992138
## 201 -0.75776 0.3693 0.63681814 0.3807483 -0.6646081 -0.0234701
## 202 -0.73712 0.3832 0.64378117 0.3610966 -0.6016185 -0.0363704
## 203 -0.87458 0.2458 0.64635790 0.6194814 -0.8619230 -0.2248306
## 204 -0.80336 0.3815 0.63592837 0.3483771 -0.6306851 -0.0253304
## 205 -0.75488 0.3530 0.70495169 0.4370346 -0.4390432 -0.0728607
## 206 -0.63589 0.6190 0.50724432 0.1311306 -0.4345183 -0.0701689
## 207 -0.70151 0.6154 0.49889048 0.1173341 -0.4670537 -0.1634384
## 208 -0.71895 0.6201 0.49240980 0.1037014 -0.4642761 -0.1419400
## 209 -0.68106 0.6157 0.50236037 0.1240150 -0.4599138 -0.0497915
## 210 -0.68640 0.3422 0.71035498 0.4759087 -0.4543869 -0.1040708
## 211 -0.66092 0.3457 0.71143942 0.4752919 -0.4339715 -0.0043869
## 212 -0.61146 0.5975 0.51599497 0.1750835 -0.4662550 -0.0813187
## 213 -0.56114 0.3475 0.73995571 0.4966764 -0.3563716 0.0477053
## 214 -0.52799 0.3338 0.74436647 0.5275368 -0.3611230 0.0812733
## 215 -0.61465 0.6133 0.51547755 0.1448668 -0.4251426 -0.0399757
## 216 -0.60685 0.3475 0.73273969 0.4861865 -0.3826482 -0.0073943
## 217 0.43761 0.3108 0.88294178 0.7878133 -0.0970118 0.5780239
## 218 0.57415 0.3695 0.95087072 0.7641316 0.2028820 -1.0709812
## 219 0.47953 0.2802 0.95413471 0.8844198 0.0792762 0.5647443
## 220 0.67390 0.2971 0.99754379 0.8936990 0.2680709 0.5835910
##
##
## Biplot scores for constraining variables
##
## dbRDA1 dbRDA2 dbRDA3 dbRDA4 dbRDA5 MDS1
## depthM -0.85366 0.18480 0.05068 0.28234 -0.2481 0
## BO22_curvelmean_ss 0.04812 -0.55958 -0.57193 0.45956 -0.2519 0
## BO22_lightbotmean_bdmean 0.46906 0.01103 0.04511 -0.36336 -0.7563 0
## BO22_tempmean_bdmean 0.18907 0.18809 0.57097 0.08928 -0.7061 0
## BO22_ppmean_bdmean -0.24726 -0.78343 -0.24709 0.11912 0.4653 0The model explains some of the genetic variation we see. Now we can run forward selection to find the best model that explains the data without over-fitting.
set.seed(092)
bestDbrda = ordiR2step(dbrda0, dbrda1, permutations = how(nperm = 99999), R2permutations = 9999, direction = "forward")## Step: R2.adj= 0
## Call: sintMa ~ 1
##
## R2.adjusted
## <All variables> 0.017110705395
## + depthM 0.014402973316
## + BO22_lightbotmean_bdmean 0.002528947465
## + BO22_ppmean_bdmean 0.001600418609
## + BO22_curvelmean_ss 0.000092235515
## <none> 0.000000000000
## + Condition(sym) 0.000000000000
## + Condition(PC1 + PC2 + PC3) 0.000000000000
## + BO22_tempmean_bdmean -0.000001733873
##
## Df AIC F Pr(>F)
## + depthM 1 607.53 4.1857 0.00001 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Step: R2.adj= 0.01440297
## Call: sintMa ~ depthM
##
## R2.adjusted
## <All variables> 0.01711071
## + BO22_ppmean_bdmean 0.01645990
## + BO22_tempmean_bdmean 0.01574441
## + Condition(sym) 0.01571710
## + BO22_lightbotmean_bdmean 0.01480829
## + BO22_curvelmean_ss 0.01479496
## <none> 0.01440297
## + Condition(PC1 + PC2 + PC3) 0.01272365
##
## Df AIC F Pr(>F)
## + BO22_ppmean_bdmean 1 608.07 1.4538 0.00975 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Step: R2.adj= 0.0164599
## Call: sintMa ~ depthM + BO22_ppmean_bdmean
##
## R2.adjusted
## + Condition(sym) 0.01772227
## + BO22_curvelmean_ss 0.01764181
## <All variables> 0.01711071
## + BO22_tempmean_bdmean 0.01698154
## <none> 0.01645990
## + BO22_lightbotmean_bdmean 0.01629163
## + Condition(PC1 + PC2 + PC3) 0.01462205bestDbrda$anova## R2.adj Df AIC F Pr(>F)
## + depthM 0.014403 1 607.53 4.1857 0.00001 ***
## + BO22_ppmean_bdmean 0.016460 1 608.07 1.4538 0.00975 **
## <All variables> 0.017111
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Depth and Primary productivity are the environmental variables that best explain the variation (although only 2.18%).
model = envData2 %>% dplyr::select("Depth" = depthM, "PP" = BO22_ppmean_bdmean, sym, PC1, PC2, PC3)
sintDbrda = dbrda(sintMa ~ Depth + PP + Condition(sym) + Condition(PC1 + PC2 + PC3), data = model)
dbrdaVarPart = varpart(as.dist(sintMa), ~ Depth, ~ PP, data = model)
dbrdaDepth = dbrda(sintMa ~ Depth + Condition(sym) + Condition(PC1 + PC2 + PC3), data = model)
dbrdaPP = dbrda(sintMa ~ PP + Condition(sym) + Condition(PC1 + PC2 + PC3), data = model)
dbrdaVarPart##
## Partition of squared Unknown user-supplied distance in dbRDA
##
## Call: varpart(Y = as.dist(sintMa), X = ~Depth, ~PP, data = model)
##
## Explanatory tables:
## X1: ~Depth
## X2: ~PP
##
## No. of explanatory tables: 2
## Total variation (SS): 16.112
## No. of observations: 219
##
## Partition table:
## Df R.squared Adj.R.squared Testable
## [a+c] = X1 1 0.01892 0.01440 TRUE
## [b+c] = X2 1 0.00618 0.00160 TRUE
## [a+b+c] = X1+X2 2 0.02548 0.01646 TRUE
## Individual fractions
## [a] = X1|X2 1 0.01486 TRUE
## [b] = X2|X1 1 0.00206 TRUE
## [c] 0 -0.00046 FALSE
## [d] = Residuals 0.98354 FALSE
## ---
## Use function 'dbrda' to test significance of fractions of interestset.seed(003)
anova(sintDbrda)## Permutation test for dbrda under reduced model
## Permutation: free
## Number of permutations: 999
##
## Model: dbrda(formula = sintMa ~ Depth + PP + Condition(sym) + Condition(PC1 + PC2 + PC3), data = model)
## Df SumOfSqs F Pr(>F)
## Model 2 0.3937 2.7126 0.001 ***
## Residual 212 15.3840
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1set.seed(002)
anova(dbrdaDepth)## Permutation test for dbrda under reduced model
## Permutation: free
## Number of permutations: 999
##
## Model: dbrda(formula = sintMa ~ Depth + Condition(sym) + Condition(PC1 + PC2 + PC3), data = model)
## Df SumOfSqs F Pr(>F)
## Model 1 0.2924 4.0218 0.001 ***
## Residual 213 15.4852
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1set.seed(001)
anova(dbrdaPP)## Permutation test for dbrda under reduced model
## Permutation: free
## Number of permutations: 999
##
## Model: dbrda(formula = sintMa ~ PP + Condition(sym) + Condition(PC1 + PC2 + PC3), data = model)
## Df SumOfSqs F Pr(>F)
## Model 1 0.0974 1.3235 0.021 *
## Residual 213 15.6802
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Now create the best model and prepare to plot
sintRdaVar = round(sintDbrda$CA$eig/sum(sintDbrda$CA$eig)*100, 1)
head(sintRdaVar)## MDS1 MDS2 MDS3 MDS4 MDS5 MDS6
## 7.6 3.5 2.9 1.6 1.3 1.1sintRdaVarFit = round(sintDbrda$CCA$eig/sum(sintDbrda$CCA$eig)*100, 1)
head(sintRdaVarFit)## dbRDA1 dbRDA2
## 79.7 20.3sintI2P = read.csv("../data/stephanocoeniaMetaData.csv")[-c(66,68,131,164,166,209,211),] %>% dplyr::select("sample" = tubeID, "pop" = site, "depth" = depthZone)
sintI2P$popdepth = paste(sintI2P$pop, sintI2P$depth)
sintRdaPoints = as.data.frame(scores(sintDbrda, choices = c(1,2,3))$sites)
# sintRdaPoints = as.data.frame(scores(sintDbrda))
sintRdaPoints$sample = sintI2P$sample
head(sintRdaPoints)## dbRDA1 dbRDA2 MDS1 sample
## 1 -0.9345180 0.38290994 0.170482268 SFK001
## 2 -0.1914385 -1.28914359 0.080333585 SFK002
## 3 -0.4439793 -1.05523131 0.081450615 SFK003
## 4 -0.6478891 -0.03986449 0.136663200 SFK004
## 5 -0.6346500 -0.30248220 0.002683636 SFK005
## 6 -0.7263526 0.16643854 0.038146344 SFK006sintDbrdaData = sintI2P %>% left_join(sintRdaPoints) %>% left_join((pcangsd %>% dplyr::select(sample, "K" = cluster)))## Joining with `by = join_by(sample)`
## Joining with `by = join_by(sample)`head(sintDbrdaData)## sample pop depth popdepth dbRDA1 dbRDA2
## 1 SFK001 Tortugas Bank Mesophotic Tortugas Bank Mesophotic -0.9345180 0.38290994
## 2 SFK002 Tortugas Bank Mesophotic Tortugas Bank Mesophotic -0.1914385 -1.28914359
## 3 SFK003 Tortugas Bank Mesophotic Tortugas Bank Mesophotic -0.4439793 -1.05523131
## 4 SFK004 Tortugas Bank Mesophotic Tortugas Bank Mesophotic -0.6478891 -0.03986449
## 5 SFK005 Tortugas Bank Mesophotic Tortugas Bank Mesophotic -0.6346500 -0.30248220
## 6 SFK006 Tortugas Bank Mesophotic Tortugas Bank Mesophotic -0.7263526 0.16643854
## MDS1 K
## 1 0.170482268 Blue
## 2 0.080333585 Teal
## 3 0.081450615 Teal
## 4 0.136663200 Blue
## 5 0.002683636 Blue
## 6 0.038146344 Bluetail(sintDbrdaData)## sample pop depth popdepth dbRDA1 dbRDA2
## 214 SFK215 Upper Keys Mesophotic Upper Keys Mesophotic -0.6080761 1.26693886
## 215 SFK216 Upper Keys Mesophotic Upper Keys Mesophotic -0.4717725 0.08138907
## 216 SFK217 Upper Keys Shallow Upper Keys Shallow -0.6446541 0.59855828
## 217 SFK218 Upper Keys Shallow Upper Keys Shallow 2.1176486 1.87153237
## 218 SFK219 Upper Keys Shallow Upper Keys Shallow -0.6295469 0.32766360
## 219 SFK220 Upper Keys Shallow Upper Keys Shallow -0.3172208 0.29777902
## MDS1 K
## 214 -0.02252615 Blue
## 215 -0.04609176 Admixed
## 216 0.45776329 Blue
## 217 -1.16359211 Green
## 218 0.43610347 Blue
## 219 0.44348830 BlueenvLoad = as.data.frame(sintDbrda$CCA$biplot)
envLoad$var = row.names(envLoad)
sintDbrdaData$depth = factor(sintDbrdaData$depth)
sintDbrdaData$depth = factor(sintDbrdaData$depth, levels(sintDbrdaData$depth)[c(2,1)])
sintDbrdaData$pop = factor(sintDbrdaData$pop)
sintDbrdaData$pop = factor(sintDbrdaData$pop, levels(sintDbrdaData$pop)[c(4, 1, 3, 2)])
head(sintDbrdaData)## sample pop depth popdepth dbRDA1 dbRDA2
## 1 SFK001 Tortugas Bank Mesophotic Tortugas Bank Mesophotic -0.9345180 0.38290994
## 2 SFK002 Tortugas Bank Mesophotic Tortugas Bank Mesophotic -0.1914385 -1.28914359
## 3 SFK003 Tortugas Bank Mesophotic Tortugas Bank Mesophotic -0.4439793 -1.05523131
## 4 SFK004 Tortugas Bank Mesophotic Tortugas Bank Mesophotic -0.6478891 -0.03986449
## 5 SFK005 Tortugas Bank Mesophotic Tortugas Bank Mesophotic -0.6346500 -0.30248220
## 6 SFK006 Tortugas Bank Mesophotic Tortugas Bank Mesophotic -0.7263526 0.16643854
## MDS1 K
## 1 0.170482268 Blue
## 2 0.080333585 Teal
## 3 0.081450615 Teal
## 4 0.136663200 Blue
## 5 0.002683636 Blue
## 6 0.038146344 BlueNow plot the dbRDA
sintDbrdaPlotA = ggplot() +
geom_hline(yintercept = 0, color = "gray90", size = 0.5) +
geom_vline(xintercept = 0, color = "gray90", size = 0.5) +
geom_point(data = sintDbrdaData, aes(x = dbRDA1, y = dbRDA2, fill = K, shape = depth), color = "black", size = 2, alpha = 1) +
scale_shape_manual(values = c(21, 23), name = "Depth Zone") +
geom_segment(data = envLoad, aes(x = 0, y = 0, xend = dbRDA1, yend = dbRDA2), color = "#F5065B", arrow = arrow(length = unit(0.15, "cm"), type = "open"), size = 0.65) +
geom_text(data = envLoad[1,], aes(x = dbRDA1-0.14, y = dbRDA2-0.08, label = var), color = "#F5065B", size = 3, fontface = "bold") +
geom_text(data = envLoad[2,], aes(x = dbRDA1-0.03, y = dbRDA2-0.1, label = var), color = "#F5065B", size = 3, fontface = "bold") +
scale_fill_manual(values = kColPal, name = "Lineage") +
scale_color_manual(values = kColPal, name = "Lineage", guide = NULL) +
labs(title = expression(italic("S. intersepta")), x = paste0("dbRDA 1 (", sintRdaVarFit[1], "% [",sintRdaVar[1], "%])"), y = paste0("dbRDA 2 (", sintRdaVarFit[2], "% [",sintRdaVar[2], "%])")) +
guides(shape = guide_legend(override.aes = list(size = 3, stroke = 0.5, alpha = 1), order = 2), fill = guide_legend(override.aes = list(shape = 22, size = 4, alpha = NA, fill = kColPal), order = 1))+
theme_bw()
sintDbrdaPlot = sintDbrdaPlotA +
theme(plot.title = element_text(color = "black", size = 12),
axis.title.x = element_text(color = "black", size = 10),
axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
axis.line.x = element_blank(),
axis.title.y = element_text(color = "black", size = 10),
axis.text.y = element_blank(),
axis.ticks.y = element_blank(),
axis.line.y = element_blank(),
legend.spacing = unit(-5, "pt"),
legend.key.size = unit(5, "pt"),
legend.position = c(1.1495, 0.5),
legend.title = element_text(size = 10),
legend.text = element_text(size = 8),
panel.border = element_rect(color = "black", size = 1),
panel.background = element_rect(fill = "white"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank())
sintDbrdaPlot
Genetic connectivity
Checking deviance among model runs from BayesAss we ran on HPC
# fileList = substr(list.files("../data/snps/bayesAss/", "BA3trace.*.txt$"),1,10)
fileList = substr(list.files("../data/snps/BA3/", "BA3trace.*.txt$"),1,11)
bayesian_deviance <- function(trace, burnin = 0, sampling.interval = 0){
if(burnin == 0) stop('No burnin specified')
if(sampling.interval == 0) stop('No sampling interval specified')
range <- (trace$State > burnin & trace$State %% sampling.interval == 0)
D <- -2*mean(trace$LogProb[range])
return(D)
}
for(i in 1:length(fileList)){
assign(fileList[i], read.delim(paste("../data/snps/BA3/", fileList[i], ".txt", sep = ""))) %>% dplyr::select(-last_col())
print(paste(fileList[i], bayesian_deviance(get(fileList[i]), burnin = 10000000, sampling.interval = 100)))
}## [1] "BA3trace.01 1078700.5909"
## [1] "BA3trace.02 1078407.0012"
## [1] "BA3trace.03 1078727.4004"
## [1] "BA3trace.04 1078794.1071"
## [1] "BA3trace.05 1078778.0004"
## [1] "BA3trace.06 1078681.4417"
## [1] "BA3trace.07 1078710.4509"
## [1] "BA3trace.08 1078817.9814"
## [1] "BA3trace.09 1078717.2801"
## [1] "BA3trace.10 1078433.2838"# [1] "BA3trace.01 1078700.5909"
# [1] "BA3trace.02 1078407.0012"
# [1] "BA3trace.03 1078727.4004"
# [1] "BA3trace.04 1078794.1071"
# [1] "BA3trace.05 1078778.0004"
# [1] "BA3trace.06 1078681.4417"
# [1] "BA3trace.07 1078710.4509"
# [1] "BA3trace.08 1078817.9814"
# [1] "BA3trace.09 1078717.2801"
# [1] "BA3trace.10 1078433.2838"All traces have similar deviance (this is good!). Using the trace with the lowest deviance (BA3trace.02.txt, in this case)
bayesAss = read.delim("../data/snps/BA3/BA3trace.02.txt") %>% filter(State > 10000000) %>% dplyr::select(-State, -LogProb, -X)
baMean = bayesAss %>% summarise(across(everything(), list(mean))) %>% t() %>% as_tibble() %>% rename(., mean=V1) %>% mutate(pops = colnames(bayesAss))
baSumm = bayesAss %>% summarise(across(everything(), list(median))) %>% t() %>% as.data.frame() %>% rename(., median=V1) %>% mutate(pops = baMean$pops, mean = round(baMean$mean, 3)) %>% relocate(median, .after = mean)
baSumm$median = round(baSumm$median, 3)
baHpd =as.data.frame(t(sapply(bayesAss, emp.hpd)))
colnames(baHpd) = c("hpdLow", "hpdHigh")
baHpd$pops = rownames(baHpd)
ESS = as.data.frame(sapply(bayesAss, ESS))
colnames(ESS) = "ESS"
baSumm = baSumm %>% left_join(baHpd)## Joining with `by = join_by(pops)`baSumm$hpdLow = round(baSumm$hpdLow, 3)
baSumm$hpdHigh = round(baSumm$hpdHigh, 3)
baSumm$ESS = ESS$ESS
### FROM BAYESASS: ###
## Population Index -> Population Label:
## 0->TortugasBank_Mesophotic 1->TortugasBank_Shallow
## 2->RileysHump_Mesophotic 3->RileysHump_Shallow
## 4->LowerKeys_Mesophotic 5->LowerKeys_Shallow
## 6->UpperKeys_Shallow 7->UpperKeys_Mesophotic
popi = rep(c("Tortugas Bank\nMesophotic", "Tortugas Bank\nShallow", "Riley's Hump\nMesophotic", "Riley's Hump\nShallow", "Lower Keys\nMesophotic", "Lower Keys\nShallow", "Upper Keys\nShallow", "Upper Keys\nMesophotic"), each = 8)
popj = rep(c("Tortugas Bank\nMesophotic", "Tortugas Bank\nShallow", "Riley's Hump\nMesophotic", "Riley's Hump\nShallow", "Lower Keys\nMesophotic", "Lower Keys\nShallow", "Upper Keys\nShallow", "Upper Keys\nMesophotic"), times = 8)
baSumm = baSumm %>% mutate(pop.i = popi, pop.j = popj) %>% relocate(c(pop.i, pop.j), .after = pops) %>% dplyr::select(-pops)
baSumm$pop.i = factor(baSumm$pop.i)
baSumm$pop.i = factor(baSumm$pop.i, levels = levels(baSumm$pop.i)[c(8, 2, 6, 4, 7, 1, 5, 3)])
baSumm$pop.j = factor(baSumm$pop.j)
baSumm$pop.j = factor(baSumm$pop.j, levels = levels(baSumm$pop.j)[c(8, 2, 6, 4, 7, 1, 5, 3)])
baSumm$site.i = word(baSumm$pop.i, 1, sep = "\n")
baSumm$site.i = factor(baSumm$site.i)
baSumm$site.i = factor(baSumm$site.i, levels = levels(baSumm$site.i)[c(4, 1, 3, 2)])
baSumm$site.j = word(baSumm$pop.j, 1, sep = "\n")
baSumm$site.j = factor(baSumm$site.j)
baSumm$site.j = factor(baSumm$site.j, levels = levels(baSumm$site.j)[c(4, 1, 3, 2)])
baSumm$depth.i = word(baSumm$pop.i, 2, sep = "\n")
baSumm$depth.i = factor(baSumm$depth.i)
baSumm$depth.i = factor(baSumm$depth.i, levels = levels(baSumm$depth.i)[c(2, 1)])
baSumm$depth.j = word(baSumm$pop.j, 2, sep = "\n")
baSumm$depth.j = factor(baSumm$depth.j)
baSumm$depth.j = factor(baSumm$depth.j, levels = levels(baSumm$depth.j)[c(2, 1)])#All sites (excluding self retention)
baMeans = baSumm %>% filter(pop.i != pop.j) %>% summarise(mean = mean(mean), sd = sd(.$mean), se = sd(.$mean)/sqrt(nrow(.))) %>% mutate(dataset = "Global")
#mesophotic sources
baMeans = baSumm %>% filter(pop.i != pop.j, depth.j == "Mesophotic") %>% summarise(mean = mean(mean), sd = sd(.$mean), se = sd(.$mean)/sqrt(nrow(.))) %>% mutate(dataset = "Mesophotic Source") %>% bind_rows(baMeans, .)
#shallow sources
baMeans = baSumm %>% filter(pop.i != pop.j, depth.j == "Shallow") %>% summarise(mean = mean(mean), sd = sd(.$mean), se = sd(.$mean)/sqrt(nrow(.))) %>% mutate(dataset = "Shallow Source") %>% bind_rows(baMeans, .)
#mesophotic sinks
baMeans = baSumm %>% filter(pop.i != pop.j, depth.i == "Mesophotic") %>% summarise(mean = mean(mean), sd = sd(.$mean), se = sd(.$mean)/sqrt(nrow(.))) %>% mutate(dataset = "Mesophotic Sink") %>% bind_rows(baMeans, .)
#shallow sinks
baMeans = baSumm %>% filter(pop.i != pop.j, depth.i == "Shallow") %>% summarise(mean = mean(mean), sd = sd(.$mean), se = sd(.$mean)/sqrt(nrow(.))) %>% mutate(dataset = "Shallow Sink") %>% bind_rows(baMeans, .)
#mesophotic -> shallow
baMeans = baSumm %>% filter(pop.i != pop.j, depth.j == "Mesophotic", depth.i == "Shallow") %>% summarise(mean = mean(mean), sd = sd(.$mean), se = sd(.$mean)/sqrt(nrow(.))) %>% mutate(dataset = "Mesophotic -> Shallow") %>% bind_rows(baMeans, .)
#mesophotic -> mesophotic
baMeans = baSumm %>% filter(pop.i != pop.j, depth.j == "Mesophotic", depth.i == "Mesophotic") %>% summarise(mean = mean(mean), sd = sd(.$mean), se = sd(.$mean)/sqrt(nrow(.))) %>% mutate(dataset = "Mesophotic -> Mesophotic") %>% bind_rows(baMeans, .)
#shallow -> mesophotic
baMeans = baSumm %>% filter(pop.i != pop.j, depth.j == "Shallow", depth.i == "Mesophotic") %>% summarise(mean = mean(.$mean), sd = sd(.$mean), se = sd(.$mean)/sqrt(nrow(.))) %>% mutate(dataset = "Shallow -> Mesophotic") %>% bind_rows(baMeans, .)
#shallow -> shallow
baMeans = baSumm %>% filter(pop.i != pop.j, depth.j == "Shallow", depth.i == "Shallow") %>% summarise(mean = round(mean(.$mean), 5), sd = round(sd(.$mean), 5), se = round(sd(.$mean)/sqrt(nrow(.)), 3)) %>% mutate(dataset = paste("Shallow -> Shallow")) %>% bind_rows(baMeans, .) %>% relocate(dataset, .before = mean) %>% as.data.frame()
baMeans[,c(2:4)] = baMeans[,c(2:4)] %>% round(4)
baMeans## dataset mean sd se
## 1 Global 0.0410 0.0572 0.0076
## 2 Mesophotic Source 0.0670 0.0725 0.0137
## 3 Shallow Source 0.0151 0.0042 0.0008
## 4 Mesophotic Sink 0.0378 0.0648 0.0122
## 5 Shallow Sink 0.0443 0.0495 0.0094
## 6 Mesophotic -> Shallow 0.0649 0.0579 0.0145
## 7 Mesophotic -> Mesophotic 0.0698 0.0912 0.0263
## 8 Shallow -> Mesophotic 0.0138 0.0049 0.0012
## 9 Shallow -> Shallow 0.0168 0.0025 0.0010baMeansTabPub = baMeans %>%
flextable() %>%
flextable::compose(part = "header", j = "dataset", value = as_paragraph("Dataset")) %>%
flextable::compose(part = "header", j = "mean", value = as_paragraph(as_i("m"))) %>%
flextable::compose(part = "header", j = "sd", value = as_paragraph("SD")) %>%
flextable::compose(part = "header", j = "se", value = as_paragraph("SEM")) %>%
flextable::font(fontname = "Times New Roman", part = "all") %>%
flextable::fontsize(size = 10, part = "all") %>%
flextable::bold(part = "header") %>%
flextable::align(align = "left", part = "all") %>%
flextable::autofit()
table3 = read_docx()
table3 = body_add_flextable(table3, value = baMeansTabPub)
print(table3, target = "../tables/table3.docx")
baMeansTabPubDataset | m | SD | SEM |
|---|---|---|---|
Global | 0.0410 | 0.0572 | 0.0076 |
Mesophotic Source | 0.0670 | 0.0725 | 0.0137 |
Shallow Source | 0.0151 | 0.0042 | 0.0008 |
Mesophotic Sink | 0.0378 | 0.0648 | 0.0122 |
Shallow Sink | 0.0443 | 0.0495 | 0.0094 |
Mesophotic -> Shallow | 0.0649 | 0.0579 | 0.0145 |
Mesophotic -> Mesophotic | 0.0698 | 0.0912 | 0.0263 |
Shallow -> Mesophotic | 0.0138 | 0.0049 | 0.0012 |
Shallow -> Shallow | 0.0168 | 0.0025 | 0.0010 |
baSumm$mean = sprintf('%.3f', baSumm$mean)
baSumm$mean2 = baSumm$mean
baSumm$hpdLow = sprintf('%.3f', baSumm$hpdLow)
baSumm$hpdHigh = sprintf('%.3f', baSumm$hpdHigh)
baLabs = tibble(pop.i = unique(baSumm$pop.i), pop.j = unique(baSumm$pop.j))
migrateA = ggplot(data = baSumm, aes(pop.i, pop.j))+
geom_tile(data = subset(baSumm, subset = baSumm$mean2>0.65), fill = "gray35", color = "white") +
geom_segment(data = baSumm, aes(x = 0.4755, xend = -0.55, y = pop.j, yend = pop.j, color = pop.j), size = 14) +
geom_segment(data = baSumm, aes(x = pop.i, xend = pop.i, y = 0.45, yend = -0.425, color = pop.i), size = 32) +
scale_color_manual(values = flPal[c(1:4, 1:4)], guide = NULL) +
guides(fill = guide_colorbar(ticks.colour = "black", barwidth = 1, barheight = 10, frame.colour = "black")) +
# new_scale("fill") +
geom_tile(data = subset(baSumm, subset = baSumm$mean<0.65), aes(fill = as.numeric(as.character(mean))), color = "white") +
scale_fill_gradientn(colours = paletteer_c("viridis::mako", n = 10, direction = -1)[c(1:7)], limit = c(0,0.27), space = "Lab", name = expression(paste(italic("m"))), na.value = "transparent", guide = "colourbar", values = c(0, 0.05, 0.1, 0.15, 0.2,0.5,0.75,1)) +
# scale_fill_gradientn(colours = paletteer_d("khroma::smoothrainbow"), limit = c(0,0.27), space = "Lab", name = expression(paste(italic("m"))), na.value = "transparent", guide = "colourbar", values = c(0, 0.05, 0.1, 0.15, 0.2,0.5,0.75,1)) +
geom_text(data = baSumm, aes(x = pop.i, y = pop.j, label = paste(mean, "\n", sep = "")), color = ifelse(baSumm$mean > 0.6, "white", "gray5"), fontface = ifelse(as.numeric(baSumm$hpdLow)>0, "bold", "plain"), size = ifelse(as.numeric(baSumm$hpdLow)>0, 4.75, 4)) +
geom_text(data = baSumm, aes(x = pop.i, y = pop.j, label = paste("\n(",hpdLow,"–",hpdHigh, ")", sep = "")), color = ifelse(baSumm$mean > 0.6, "white", "gray5"), size = 3.25) +
geom_text(data = (baLabs %>% filter(pop.j %in% c("Tortugas Bank\nMesophotic", "Tortugas Bank\nShallow", "Riley's Hump\nMesophotic", "Riley's Hump\nShallow"))), x = -.02, aes(y = pop.j, label = pop.j), size = 3.75, color = "#FFFFFF", family = "sans") +
geom_text(data = (baLabs %>% filter(!pop.j %in% c("Tortugas Bank\nMesophotic", "Tortugas Bank\nShallow", "Riley's Hump\nMesophotic", "Riley's Hump\nShallow"))), x = -.02, aes(y = pop.j, label = pop.j), size = 3.75, color = "#000000", family = "sans") +
geom_text(data = (baLabs %>% filter(pop.i %in% c("Tortugas Bank\nMesophotic", "Tortugas Bank\nShallow", "Riley's Hump\nMesophotic", "Riley's Hump\nShallow"))), y = -.01, aes(x = pop.i, label = pop.i), size = 3.75, color = "#FFFFFF", family = "sans") +
geom_text(data = (baLabs %>% filter(!pop.i %in% c("Tortugas Bank\nMesophotic", "Tortugas Bank\nShallow", "Riley's Hump\nMesophotic", "Riley's Hump\nShallow"))), y = -.01, aes(x = pop.i, label = pop.i), size = 3.75, color = "#000000", family = "sans") +
labs(x = "Sink", y = "Source") +
scale_y_discrete(limits = rev(levels(baSumm$pop.i))[c(1:8)], position = "left") +
coord_cartesian(xlim = c(1, 8), ylim = c(1, 8), clip = "off") +
theme_minimal()
migrate = migrateA + theme(
axis.text.x = element_text(vjust = 1, size = 12, hjust = 0.5, color = NA),
axis.text.y = element_text(size = 10, color = NA),
axis.title.x = element_text(size = 14),
axis.title.y = element_text(size = 14),
panel.grid.major = element_blank(),
axis.ticks = element_blank(),
# legend.position = c(1.055, 0.5),
legend.direction = "vertical",
legend.title = element_text(size = 12, face = "bold")
)
migrate
baSumm$mean = as.numeric(baSumm$mean)
baSumm$hpdLow = as.numeric(baSumm$hpdLow)
baSumm$hpdHigh = as.numeric(baSumm$hpdHigh)
baSummSelf = baSumm %>% filter(pop.i == pop.j) %>% mutate(popdepth = paste(site.i, depth.i)) %>% mutate(retention = mean) %>% dplyr::select(-mean)
fknmsPopsMigrate2 = fknmsSites %>% group_by(site, depthZone, siteID) %>% dplyr::summarise(latDD = first(latDD), longDD = first(longDD)) %>% dplyr::filter(siteID %in% c("Ian's Lumps Site 52", "Site 48", "Site 47", "Site 45", "Site 35/36", "Site 37", "Site 39", "Site 19")) %>% dplyr::select(-site) %>% droplevels() %>% mutate(popdepth = paste(site, depthZone)) %>% as.data.frame() %>% slice(-5) %>% left_join(dplyr::select(baSummSelf, popdepth, retention))## `summarise()` has grouped output by 'site', 'depthZone'. You can override using the
## `.groups` argument.
## Adding missing grouping variables: `site`
## Joining with `by = join_by(popdepth)`fknmsPopsMigrate = fknmsPopsMigrate2[c(1,2,4,3,5:8),]
migratePal = c("Upper Keys" = flPal[1], "Lower Keys" = flPal[2], "Tortugas Bank" = flPal[3], "Riley's Hump" = flPal[4])
lines = c("Shallow" = 5, "Mesophotic" = 1)
baMapData = dplyr::select(baSumm, -mean2) %>% left_join(dplyr::select(fknmsPopsMigrate,-retention,-popdepth), by = c("site.i" = "site", "depth.i" = "depthZone")) %>% left_join(dplyr::select(fknmsPopsMigrate,-retention,-popdepth),, by = c("site.j" = "site", "depth.j" = "depthZone"), suffix = c(".i", ".j")) %>% filter(mean >= 0.02)
for(x in 1:nrow(baMapData)) {
if (baMapData$pop.i[x] == baMapData$pop.j[x]) {
baMapData$latDD.i[x] = NA;
baMapData$latDD.j[x] = NA;
baMapData$longDD.i[x] = NA;
baMapData$longDD.j[x] = NA;
baMapData$mean[x] = NA;
baMapData$median[x] = NA
}
}
migrateMap = ggplot() +
geom_sf(data = florida, fill = "white", size = 0.25) +
# SHALLOW SOURCES
geom_curve(data = baMapData[6,], aes(x = longDD.j, y = latDD.j, xend = longDD.i-0.02, yend = latDD.i, color = site.j, linetype = depth.j, size = mean), alpha = 0.7, arrow = arrow(type = "open", length = unit(0.03, "npc")), curvature = -1.5) +
geom_curve(data = baMapData[8,], aes(x = longDD.j, y = latDD.j, xend = longDD.i+0.01, yend = latDD.i, color = site.j, linetype = depth.j, size = mean), alpha = 0.7, arrow = arrow(type = "open", length = unit(0.03, "npc")), curvature = -0.2) +
geom_curve(data = baMapData[9,], aes(x = longDD.j, y = latDD.j, xend = longDD.i+0.02, yend = latDD.i, color = site.j, linetype = depth.j, size = mean), alpha = 0.7, arrow = arrow(type = "open", length = unit(0.03, "npc")), curvature = 0.2) +
geom_curve(data = baMapData[12,], aes(x = longDD.j, y = latDD.j, xend = longDD.i-0.03, yend = latDD.i, color = site.j, linetype = depth.j, size = mean), alpha = 0.7, arrow = arrow(type = "open", length = unit(0.03, "npc")), curvature = 1.1) +
geom_curve(data = baMapData[14,], aes(x = longDD.j, y = latDD.j, xend = longDD.i, yend = latDD.i-0.02, color = site.j, linetype = depth.j, size = mean), alpha = 0.7, arrow = arrow(type = "open", length = unit(0.03, "npc")), curvature = -4) +
geom_curve(data = baMapData[16,], aes(x = longDD.j, y = latDD.j, xend = longDD.i+0.01, yend = latDD.i, color = site.j, linetype = depth.j, size = mean), alpha = 0.7, arrow = arrow(type = "open", length = unit(0.03, "npc")), curvature = -0.1) +
geom_curve(data = baMapData[17,], aes(x = longDD.j, y = latDD.j, xend = longDD.i+0.01, yend = latDD.i, color = site.j, linetype = depth.j, size = mean), alpha = 0.7, arrow = arrow(type = "open", length = unit(0.03, "npc")), curvature = 0.2) +
# MESO SOURCES
geom_curve(data = baMapData[2,], aes(x = longDD.j, y = latDD.j, xend = longDD.i+0.01, yend = latDD.i, color = site.j, linetype = depth.j, size = mean), alpha = 0.7, arrow = arrow(type = "open", length = unit(0.03, "npc")), curvature = 0.2) +
geom_curve(data = baMapData[3,], aes(x = longDD.j, y = latDD.j, xend = longDD.i+0.01, yend = latDD.i, color = site.j, linetype = depth.j, size = mean), alpha = 0.7, arrow = arrow(type = "open", length = unit(0.03, "npc")), curvature = -0.2) +
geom_curve(data = baMapData[5,], aes(x = longDD.j, y = latDD.j, xend = longDD.i-0.01, yend = latDD.i, color = site.j, linetype = depth.j, size = mean), alpha = 0.7, arrow = arrow(type = "open", length = unit(0.03, "npc")), curvature = -1) +
geom_curve(data = baMapData[7,], aes(x = longDD.j, y = latDD.j, xend = longDD.i+0.01, yend = latDD.i, color = site.j, linetype = depth.j, size = mean), alpha = 0.7, arrow = arrow(type = "open", length = unit(0.03, "npc")), curvature = 0.4) +
geom_curve(data = baMapData[10,], aes(x = longDD.j, y = latDD.j, xend = longDD.i+0.01, yend = latDD.i, color = site.j, linetype = depth.j, size = mean), alpha = 0.7, arrow = arrow(type = "open", length = unit(0.03, "npc")), curvature = -0.3) +
geom_curve(data = baMapData[11,], aes(x = longDD.j, y = latDD.j, xend = longDD.i-0.01, yend = latDD.i, color = site.j, linetype = depth.j, size = mean), alpha = 0.7, arrow = arrow(type = "open", length = unit(0.03, "npc")), curvature = 1.4) +
geom_curve(data = baMapData[15,], aes(x = longDD.j, y = latDD.j, xend = longDD.i+0.02, yend = latDD.i, color = site.j, linetype = depth.j, size = mean), alpha = 0.7, arrow = arrow(type = "open", length = unit(0.03, "npc")), curvature = 0.2) +
geom_curve(data = baMapData[18,], aes(x = longDD.j, y = latDD.j, xend = longDD.i+0.01, yend = latDD.i, color = site.j, linetype = depth.j, size = mean), alpha = 0.7, arrow = arrow(type = "open", length = unit(0.03, "npc")), curvature = -0.2) +
geom_curve(data = baMapData[20,], aes(x = longDD.j, y = latDD.j, xend = longDD.i+0.01, yend = latDD.i, color = site.j, linetype = depth.j, size = mean), alpha = 0.7, arrow = arrow(type = "open", length = unit(0.03, "npc")), curvature = 0.3) +
geom_curve(data = baMapData[21,], aes(x = longDD.j, y = latDD.j, xend = longDD.i+0.01, yend = latDD.i, color = site.j, linetype = depth.j, size = mean), alpha = 0.7, arrow = arrow(type = "open", length = unit(0.03, "npc")), curvature = -0.2) +
geom_curve(data = baMapData[24,], aes(x = longDD.j, y = latDD.j, xend = longDD.i-0.01, yend = latDD.i, color = site.j, linetype = depth.j, size = mean), alpha = 0.7, arrow = arrow(type = "open", length = unit(0.03, "npc")), curvature = 0.2) +
geom_curve(data = baMapData[25,], aes(x = longDD.j, y = latDD.j, xend = longDD.i-0.01, yend = latDD.i, color = site.j, linetype = depth.j, size = mean), alpha = 0.7, arrow = arrow(type = "open", length = unit(0.03, "npc")), curvature = -6) +
geom_curve(data = baMapData[27,], aes(x = longDD.j, y = latDD.j, xend = longDD.i+0.01, yend = latDD.i-0.01, color = site.j, linetype = depth.j, size = mean), alpha = 0.7, arrow = arrow(type = "open", length = unit(0.03, "npc")), curvature = -0.4) +
geom_curve(data = baMapData[28,], aes(x = longDD.j, y = latDD.j, xend = longDD.i-0.01, yend = latDD.i, color = site.j, linetype = depth.j, size = mean), alpha = 0.7, arrow = arrow(type = "open", length = unit(0.03, "npc")), curvature = 0.2) +
geom_curve(data = baMapData[30,], aes(x = longDD.j, y = latDD.j, xend = longDD.i+0.02, yend = latDD.i, color = site.j, linetype = depth.j, size = mean), alpha = 0.7, arrow = arrow(type = "open", length = unit(0.03, "npc")), curvature = 2) +
geom_curve(data = baMapData[31,], aes(x = longDD.j, y = latDD.j, xend = longDD.i-0.01, yend = latDD.i-0.01, color = site.j, linetype = depth.j, size = mean), alpha = 0.7, arrow = arrow(type = "open", length = unit(0.03, "npc")), curvature = 0.3) +
geom_curve(data = baMapData[23,], aes(x = longDD.j, y = latDD.j, xend = longDD.i+0.01, yend = latDD.i, color = site.j, linetype = depth.j, size = mean), alpha = 0.7, arrow = arrow(type = "open", length = unit(0.03, "npc")), curvature = -0.1) +
scale_fill_manual(values = migratePal, name = "Source site") +
scale_color_manual(values = migratePal, guide = NULL) +
scale_shape_manual(values = c(21, 23), name = "Depth") +
scale_size(range = c(0.5, 2), breaks = c(0.02,0.06,0.1,0.14,0.18,0.22,0.26),name = expression(paste("Migration (", italic("m"), ")", sep = "")), guide = guide_legend(ncol = 1, order = 5)) +
geom_point(data = fknmsPopsMigrate, aes(x = longDD, y = latDD, fill = site, shape = depthZone), size = 3.5) +
scale_linetype_manual(values = lines, name = "Source depth") +
coord_sf(xlim = c(-83.1, -80.25), ylim = c(24.3, 25.3)) +
scale_x_continuous(breaks = c(seq(-84, -80, by = .5))) +
scale_y_continuous(breaks = c(seq(24, 26, by = .2))) +
annotation_scale(location = "br") +
annotation_north_arrow(location = "br", which_north = "true", style = north_arrow_minimal(), pad_x = unit(-0.25, "cm") , pad_y = unit(0.75, "cm")) +
guides(fill = guide_legend(override.aes = list(shape = 22, color = NA, size = 4),ncol = 1, order = 1, reverse = TRUE), shape = guide_legend(override.aes = list(size = 3), order = 2)) +
theme_bw() +
theme(panel.background = element_rect(fill = "aliceblue"),
panel.border = element_rect(color = "black", size = 0.75, fill = NA),
plot.background = element_blank(),
axis.title = element_blank(),
axis.ticks = element_line(color = "black"),
axis.text = element_text(color = "black"),
plot.title = element_blank(),
legend.key.size = unit(15, "pt"),
legend.spacing = unit(-5, "pt"),
legend.position = "right",
legend.direction = "vertical",
legend.box = "vertical",
legend.background = element_blank()
)
migrateMap
Putting the plots together into a single figure panel
migrationPlots = (migrate/(migrateMap) + theme(legend.box.margin = margin(-20, 0, 0, 0))) + plot_layout(heights = c(1, 1.1)) + plot_annotation(tag_levels = 'A') & theme(plot.tag = element_text(size = 20))
ggsave("../figures/figure5.png", plot = migrationPlots, width = 28, height = 24, units = "cm", dpi = 300)
ggsave("../figures/figure5.svg", plot = migrationPlots, width = 28, height = 25, units = "cm", dpi = 300)S. intersepta algal symbiont community structure
Now let’s examine algal symbiont communities with the results of SymPortal analysis of Symbiodiniaceae ITS2 sequences.
ITS2 Data
How many raw reads?
rawItsReads = read.delim("../data/ITS2/sintItsReadCounts", header = FALSE)
colnames(rawItsReads) = c("sample", "reads")
rawItsReads$sample = gsub("_S.*", "", rawItsReads$sample)
rawItsReads = rawItsReads %>% group_by(sample) %>% summarise(reads = first(reads))
head(rawItsReads)## # A tibble: 6 × 2
## sample reads
## <chr> <int>
## 1 SFK001 34806
## 2 SFK002 38328
## 3 SFK003 18122
## 4 SFK004 52311
## 5 SFK005 30791
## 6 SFK006 40545#total reads
sum(rawItsReads$reads)## [1] 7385411#average reads/sample
(sum(rawItsReads$reads)/nrow(rawItsReads))## [1] 33723.34its2Seqs = read.delim("../data/ITS2/148_20210301_DBV_20210401T112728.seqs.absolute.abund_CLEAN.txt", header = TRUE)
its2Profs = read.csv("../data/ITS2/148_20210301_DBV_20210401T112728.profiles.absolute.abund_CLEAN.csv", header = TRUE, check.names = FALSE)
head(its2Seqs)## Sample Symbiodinium A3 A3b A3at A3ax X43947_A X34778_A X495083_A X36534_A X34149_A
## 1 SFK115 0 12 0 0 0 0 0 0 0 0
## 2 SFK022 0 7 0 0 0 0 0 0 0 0
## 3 SFK025 28 242 7 7 0 6 0 0 7 0
## 4 SFK095 0 14 0 0 0 0 0 0 0 0
## 5 SFK170 0 26 0 0 0 0 0 0 0 0
## 6 SFK175 0 5 0 0 0 0 0 0 0 0
## X50854_A A3av A3s A3q X33981_A X1402229_A A3au A3aw X50835_A X34175_A X33953_A A3r
## 1 0 0 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 17 23 5 0 0 0 0 0 0 6
## 4 0 0 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0 0 0
## X1402205_A X364481_A X363583_A A4 X1402230_A X50833_A X50842_A X363143_A X72388_A
## 1 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 10 0
## 4 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0
## 6 0 0 0 7 0 0 0 0 0
## X363606_A X797686_A X22386_A X529468_A X34696_A X363636_A X1402231_A X34151_A
## 1 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0
## X364459_A X363570_A X363645_A X363578_A X1402232_A X364267_A X363625_A X50845_A
## 1 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0
## X363142_A X1402267_A X22415_A X363639_A X905679_A X363598_A X363706_A X363685_A
## 1 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0
## X1402206_A X1402233_A X1402235_A X43753_A X500385_A X1402234_A A3d X1402202_A
## 1 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0
## X1402236_A X364620_A X363593_A X367833_A X22400_A X50850_A X22463_A X363687_A
## 1 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0
## X693524_A X37988_A X66961_A X22436_A X363590_A X22426_A X45527_A A6b X36953_A
## 1 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0
## X363563_A X37985_A X693526_A X22451_A X33927_A X1402203_A A4.3 X35200_A X22392_A
## 1 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0
## X65140_A X1402245_A X364567_A X364639_A X1402216_A X22464_A X22429_A X49905_A
## 1 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0
## X49571_A X29211_A X36825_A X364218_A X1402237_A X363617_A X73521_A X364172_A
## 1 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0
## X363654_A X1402274_A X49906_A X37990_A X50843_A X363674_A X363646_A X33878_A
## 1 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0
## X1402268_A X1402282_A X22444_A X373280_A X1402238_A X366219_A X69439_A Breviolum B5
## 1 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0
## B18c B18b X1402208_B X1402209_B X45548_B X1402210_B X43411_B X37534_B X1402211_B
## 1 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0
## X1402212_B X1402213_B X1160454_B X1402214_B X1402215_B X37591_B B5ai X1402254_B
## 1 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0
## X71511_B X1402256_B X71527_B X1402255_B X1402257_B X71517_B X71509_B X71508_B
## 1 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0
## X1402258_B X71518_B X71510_B X1402259_B X368876_B X1402260_B X50427_B X1402262_B
## 1 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0
## X71525_B X71523_B X71515_B X368004_B X1402261_B X71526_B X71524_B X1402266_B
## 1 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0
## X1402265_B X1402264_B X1402263_B X43545_B X1402252_B X900132_B X1390171_B X1402253_B
## 1 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0
## B1 X1402276_B X1402280_B X38112_B Cladocopium C3 C1 C16 C3go C3.10 C42.2 C1dl C3gm
## 1 0 0 0 0 761 10037 0 0 0 123 0 0 0
## 2 0 0 0 0 1273 15896 0 0 0 260 0 0 0
## 3 0 0 0 0 661 11333 0 0 0 115 0 0 0
## 4 0 0 0 0 1019 13663 0 0 0 717 0 0 0
## 5 0 0 0 0 1077 12007 0 0 0 619 0 0 0
## 6 0 0 0 0 1975 21478 0 0 0 385 0 0 110
## C3gl C3hb C3gr C16b X110271_C X334025_C C3gk C1dk X22330_C X11408_C X18596_C X21897_C
## 1 0 0 0 0 0 0 0 0 58 0 0 90
## 2 0 0 0 0 164 158 0 0 150 0 0 157
## 3 0 0 0 0 67 68 0 0 85 0 0 91
## 4 0 0 0 0 0 0 0 0 0 0 0 227
## 5 0 0 0 0 102 120 0 0 83 0 0 93
## 6 0 0 0 0 282 260 0 0 210 0 0 274
## C6c C3gq C3gp X65808_C C3gn C15hx C3dw C1cy X1402187_C X20795_C C93.1 X65703_C
## 1 0 0 0 54 0 0 0 0 181 0 0 144
## 2 0 0 0 0 0 0 0 0 120 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 68
## 4 0 0 0 88 0 0 0 0 133 0 229 96
## 5 0 0 0 106 0 0 0 0 131 0 81 120
## 6 0 0 0 166 0 0 0 0 303 0 0 0
## X385070_C C3ge X1402188_C X1372_C X3238_C X95094_C C3hc X24879_C X91373_C X3699_C
## 1 0 0 107 0 0 0 0 0 0 0
## 2 0 0 93 0 135 0 0 188 0 108
## 3 0 0 76 0 0 0 0 100 0 0
## 4 0 0 120 185 0 0 0 0 0 0
## 5 0 0 89 82 0 0 0 0 0 0
## 6 0 0 128 0 0 0 0 120 0 0
## C3gt C3dz X20934_C C1af C3gs X25557_C X40208_C X470358_C X40209_C X1402193_C X2239_C
## 1 0 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 170 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0 0
## 6 0 0 232 0 0 0 0 0 0 0 0
## X1402195_C C16a X17495_C X17534_C X2097_C X40211_C X93722_C C1v X40207_C X40212_C
## 1 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0
## 3 55 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0
## X1402196_C X1402197_C X9944_C X1402198_C X1402219_C X470998_C X54162_C X22574_C
## 1 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0
## 5 0 0 94 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0
## X20921_C X33343_C X1402200_C X25492_C X1402218_C X3240_C X2037_C X85729_C X5371_C
## 1 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0
## 3 0 0 0 51 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0
## 5 0 0 0 57 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0
## X1402225_C X909389_C X1402207_C C3ag X2428_C X1402220_C X4062_C X103828_C X1402199_C
## 1 0 0 0 0 0 0 0 127 0
## 2 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0
## X1398518_C X90670_C X1402204_C X1402227_C X1402248_C C6b X1402247_C X1402194_C X870_C
## 1 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0
## X71029_C C3ga X91285_C X1402192_C X1402244_C C1bz X18746_C X1402228_C X694_C C3i
## 1 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0
## X1402250_C X1402243_C C21 X1402281_C C3ca X9108_C X11201_C X11191_C X7821_C
## 1 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0
## X1402273_C C3ck X1402240_C X1402249_C X99988_C X1356_C X69324_C X24193_C C3bb C40f
## 1 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0
## X1401572_C X47282_C X16815_C X5726_C X1402270_C X1402221_C C1ap X1402275_C X21673_C
## 1 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0
## X69758_C C1bt X1402269_C X2943_C C70 X1402271_C X42218_C X1402277_C X9807_C C1ai C3t
## 1 0 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0 0
## X54249_C X26258_C X1402278_C X1402272_C C1x X40218_C X21804_C X1402279_C C3de
## 1 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0
## X13929_C X23354_C X1402223_C X99010_C X983542_C X3366_C X1402226_C X1402222_C
## 1 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0
## X42529_C X2152_C X62532_C X4558_C X2427_C X1829_C C3fo X18793_C X11809_C X31248_C
## 1 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0
## X1402241_C X816_C X921460_C C3ao C3an C3cn X3241_C X103581_C X21093_C X1402224_C
## 1 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0
## X18813_C X1402201_C X22869_C X23865_C C6a C1j X1402239_C X3434_C X22178_C X17016_C
## 1 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0
## X1402242_C X42518_C X54160_C X873_C C50f X1402246_C X1390080_C X113247_C X99987_C
## 1 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0
## X3601_C X1866_C X1402251_C X2895_C X9153_C C3fn X866_C X864_C X18159_C X990_C
## 1 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0
## X1402189_C X21205_C C3fc X871_C X1402190_C X8117_C X55844_C X54218_C X51874_C
## 1 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0
## X874099_C X27927_C C65b X46049_C X37410_C X28411_C D1 G3l G3b
## 1 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0sum(its2Seqs[,c(2:ncol(its2Seqs))])## [1] 3280586sum(its2Profs[,c(2:ncol(its2Profs))])## [1] 2728837its2SeqsGen = its2Seqs %>% rowwise() %>% summarise(sample = Sample, symbiodinium = sum(c_across(2:121)), breviolum = sum(c_across(122:176)), cladocopium = sum(c_across(177:405)), durusdinium = sum(c_across(406)), gerakladium = sum(c_across(406:407)))
round(sum(its2SeqsGen$symbiodinium)/sum(its2SeqsGen[,-1])*100, 2)## [1] 19.67round(sum(its2SeqsGen$breviolum)/sum(its2SeqsGen[,-1])*100, 2)## [1] 0.29round(sum(its2SeqsGen$cladocopium)/sum(its2SeqsGen[,-1])*100, 2)## [1] 80.03round(sum(its2SeqsGen$durusdinium)/sum(its2SeqsGen[,-1])*100, 4)## [1] 0.0002round(sum(its2SeqsGen$gerakladium)/sum(its2SeqsGen[,-1])*100, 4) ## [1] 0.002its2ProfsGen = its2Profs %>% rowwise() %>% summarise(sample = Sample, symbiodinium = sum(c_across(2:7)), breviolum = sum(c_across(8:10)), cladocopium = sum(c_across(11:20)))
round(sum(its2ProfsGen$symbiodinium)/sum(its2ProfsGen[,-1])*100, 2)## [1] 21.33round(sum(its2ProfsGen$breviolum)/sum(its2ProfsGen[,-1])*100, 2)## [1] 0.22round(sum(its2ProfsGen$cladocopium)/sum(its2ProfsGen[,-1])*100, 2)## [1] 78.45Read in SymPortal outputs for ITS2 type profiles
stephanocoeniaMetaData = read.csv("../data/stephanocoeniaMetaData.csv", header = TRUE, check.names = FALSE)[-c(66, 68, 164, 166, 209, 211),] %>% dplyr::select(c(sample = tubeID, site, depthM, depthZone))
its2Profs = read.csv("../data/ITS2/148_20210301_DBV_20210401T112728.profiles.absolute.abund_CLEAN.csv", header = TRUE, check.names = FALSE) %>% rename(sample = Sample)
its2Profs = stephanocoeniaMetaData %>% right_join(its2Profs) %>% arrange(sample) ## Joining with `by = join_by(sample)`its2Profs$site = factor(its2Profs$site)
its2Profs$site = factor(its2Profs$site, levels(its2Profs$site)[c( 2, 3, 1, 4)])
its2Profs$depthZone = factor(its2Profs$depthZone)
its2Profs$depthZone = factor(its2Profs$depthZone, levels(its2Profs$depthZone)[c(2, 1)])
its2Profs = its2Profs %>% arrange(site, depthZone, desc(`C3/C3.10`), desc(`C1/C3-C42.2-C1dl-C3gl-C3gm-C3gk`), desc(`C3-C1-C3.10`), desc(`C3-C1dk-C15hx`), desc(`C3-C3go-C6c-C3gq-C3gp-C3gn-C3dw`), desc(`C16/C3-C16b`), desc(`C3-C3hb-C3ge-C3hc-C1dk`), desc(`C3-C3gr-C3gt-C3gs-C3.10`), desc(`C3/C1`),desc(`A3-A3b-A3at-A3ax`), desc(`A3-A3at-A3b-A3q-A3s`), desc(`A3-A3s-A3q`), desc(`A3`), desc(`A3-A3b-A3av-A3au-A3aw`),desc(`A4`), desc(`C3`), desc(`B18b`), desc(`B18c`), desc(`B5`))
sampleCounts = plyr::count(its2Profs, c('site','depthZone'))
meltedList = reshape2::melt(lapply(sampleCounts$freq,function(x){c(1:x)}))
its2Profs$barPlotOrder = meltedList$value
its2Profs = its2Profs[c(1,ncol(its2Profs),2:(ncol(its2Profs)-1))]
head(its2Profs)ITS2 type profiles
Preparing ITS2 type profiles for plotting
its2ProfsPerc = its2Profs
its2ProfsPerc$sum = apply(its2ProfsPerc[, c(6:length(its2ProfsPerc[1,]))], 1, function(x) {
sum(x, na.rm = T)
})
its2ProfsPerc = cbind(its2ProfsPerc[, c(1:5)], (its2ProfsPerc[, c(6:(ncol(its2ProfsPerc)-1))]
/ its2ProfsPerc$sum))
head(its2ProfsPerc)## sample barPlotOrder site depthM depthZone A3-A3b-A3at-A3ax
## 1 SFK068 1 Riley's Hump 27.4 Shallow 0
## 2 SFK091 2 Riley's Hump 26.2 Shallow 0
## 3 SFK073 3 Riley's Hump 26.2 Shallow 0
## 4 SFK084 4 Riley's Hump 26.2 Shallow 0
## 5 SFK083 5 Riley's Hump 26.2 Shallow 0
## 6 SFK082 6 Riley's Hump 26.5 Shallow 0
## A3-A3at-A3b-A3q-A3s A3-A3s-A3q A3 A3-A3b-A3av-A3au-A3aw A4 B18b B18c B5 C3/C3.10
## 1 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0
## C1/C3-C42.2-C1dl-C3gl-C3gm-C3gk C3-C1-C3.10 C3-C1dk-C15hx
## 1 1 0 0
## 2 1 0 0
## 3 1 0 0
## 4 1 0 0
## 5 1 0 0
## 6 1 0 0
## C3-C3go-C6c-C3gq-C3gp-C3gn-C3dw C16/C3-C16b C3-C3hb-C3ge-C3hc-C1dk
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## C3-C3gr-C3gt-C3gs-C3.10 C3/C1 C3
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0# check that all proportions add up to 1
apply(its2ProfsPerc[, c(6:(ncol(its2ProfsPerc)))], 1, function(x) {
sum(x, na.rm = T)
})## [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [42] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [83] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [124] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [165] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [206] 1 1 1 1 1 1 1 1 1 1 1 1 1 1admixOrd = fkSintAdmix %>% dplyr::select(sample, ord)
pcangsdITS = pcangsd %>% dplyr::select(sample, cluster)
its2ProfsPerc = its2ProfsPerc %>% left_join(admixOrd) %>% relocate(ord,.after = barPlotOrder) %>% left_join(pcangsdITS) %>% relocate(cluster, .after = depthZone)## Joining with `by = join_by(sample)`
## Joining with `by = join_by(sample)`Everything looks good and is ready to plot
gssProf = otuStack(its2ProfsPerc, count.columns = c(8:length(its2ProfsPerc[1, ])),
condition.columns = c(1:7)) %>% filter(otu != "summ") %>% droplevels() # remove summ rows
levels(gssProf$otu)
levels(gssProf$depthZone)
levels(gssProf$site)Consruct ITS2 type profile barplot
zooxAnno = data.frame(x1 = c(0.5, 0.5, 0.5, 0.5), x2 = c(30.5, 30.5, 30.5, 30.5),
y1 = -0.22, y2 = -0.22, site = c("Riley's Hump", "Tortugas Bank", "Lower Keys", "Upper Keys"))
zooxAnno$site = factor(zooxAnno$site)
zooxAnno$site = factor(zooxAnno$site, levels = levels(zooxAnno$site)[c(2, 3, 1, 4)])
gssProfPlot = gssProf %>% left_join(zooxAnno, by = "site")
gssProfPlot$ord = as.numeric(gssProfPlot$ord)
its2ProfsPlotA = ggplot(gssProfPlot, aes(x = ord, y = count, fill = otu)) +
geom_bar(position = "stack", stat = "identity", color = "gray25", size = 0.25) +
scale_fill_manual(values = profPal, name = expression(paste(italic("ITS2"), " type profile"))) +
geom_segment(data = gssProfPlot, aes(x = ord-0.5, xend = ord+0.5, color = cluster), y = -.07, yend = -.07, linewidth = 4) +
scale_color_manual(values = kColPal, guide = "none") +
ggnewscale::new_scale_color() +
geom_segment(data = gssProfPlot %>% filter(depthZone == "Mesophotic"), aes(x = x1, xend = x2, y = y1, yend = y2, color = site), linewidth = 7) +
scale_color_manual(values = rev(flPal)) +
geom_text(data = (gssProfPlot %>% filter(depthZone == "Mesophotic", site %in% c("Riley's Hump", "Tortugas Bank"), sample %in% c("SFK001", "SFK100"), otu == "A4")), x = 15.5, y = -.205, aes(label = site), size = 4, color = "#FFFFFF") +
geom_text(data = (gssProfPlot %>% filter(depthZone == "Mesophotic", site %in% c("Lower Keys", "Upper Keys"), sample %in% c("SFK101", "SFK201"), otu == "A4")), x = 15.5, y = -.205, aes(label = site), size = 4, color = "#000000") +
labs(title = expression(italic("SymPortal")), fill = expression(paste(italic("ITS2"), " type profile"))) +
guides(color = "none", fill = guide_legend(ncol = 3, reverse = FALSE)) +
facet_grid(factor(depthZone) ~ site, scales = "free", switch = "both", space = "free") + # faceting plots by Depth and Site
coord_cartesian(ylim = c(0, 1), xlim = c(0.5, 30.5), clip = "off") +
scale_x_discrete(expand = c(0.005, 0.005)) +
scale_y_continuous(expand = c(0.001, 0.001)) +
theme_bw()
its2ProfsPlot = its2ProfsPlotA +
theme(plot.title = element_text(),
panel.grid = element_blank(),
# panel.background = element_blank(),
panel.background = element_rect(fill = "gray70"),
panel.border = element_rect(fill = NA, color = "black", size = 0.75, linetype = "solid"),
plot.background = element_blank(),
legend.background = element_blank(),
panel.spacing.x = grid:::unit(0.05, "lines"),
panel.spacing.y = grid:::unit(0.78, "lines"),
axis.text.x = element_blank(),
axis.text.y = element_blank(),
axis.ticks.x = element_blank(),
axis.ticks.y = element_blank(),
axis.title = element_blank(),
strip.background.x = element_blank(),
strip.background.y = element_blank(),
strip.text = element_text(size = 12),
strip.text.y.left = element_text(angle = 90),
strip.text.x.bottom = element_text(vjust = .75, color = NA),
legend.key.size = unit(0.75, "line"),
legend.key = element_blank(),
legend.title = element_text(size = 10, angle = 90),
legend.text = element_text(size = 8),
legend.position = "right")
# its2ProfsPlot
Structure plots
structurePlots = ((pcaPlot12S + theme(axis.title.y = element_text(margin = ggplot2::margin(r = -20, unit = "pt")))) | pcaPlot12L | pcaPlot23L)/((admixPlot + labs(title = expression(paste(italic("S. intersepta"))))) | (its2ProfsPlot + guides(color = "none", fill = guide_legend(ncol = 1, reverse = FALSE)) + labs(title = "Symbiodiniaceae", fill = expression(paste(italic("ITS2"), " type profile"))) + theme(legend.title = element_text(size = 8, angle = 0), plot.title = element_text(size = 10), legend.text = element_text(size = 6), strip.text.y.left = element_text(angle = 90, size = 10),strip.text.x = element_text(size = 8)))) +
plot_annotation(tag_levels = 'A') &
theme(plot.tag = element_text(size = 16),
plot.title = element_text(size = 10),
plot.title.position = "panel",
legend.spacing = unit(-5, "pt"),
legend.key = element_blank(),
legend.background = element_blank())
ggsave("../figures/figure2.png", plot = structurePlots, height = 6.6, width = 12, units = "in", dpi = 300)
ggsave("../figures/figure2.svg", plot = structurePlots, height = 6.5, width = 10, units = "in", dpi = 300)SNP vs ITS2 genera
Pulling genera of Symbiodiniaceae from SNPS and comparing to genera
of ITS2 profiles from SymPortal
popData = read.csv("../data/stephanocoeniaMetaData.csv")[-c(66, 68, 164, 166, 209, 211),] %>% dplyr::select("sample" = tubeID, "site" = site, "depth" = depthZone)
zoox = read.delim("../data/snps/symbionts/zooxReads", header = FALSE, check.names = FALSE)
head(zoox)## V1 V2
## 1 fk_S001.trim.zoox.zoox.bt2.bam NA
## 2 chr1 77
## 3 chr2 78
## 4 chr3 87
## 5 chr4 80
## 6 chr5 2# Reconstruct read mapping output into dataframe usable for analysis
zoox$V2[is.na(zoox$V2)] <- as.character(zoox$V1[is.na(zoox$V2)])
zoox$V1 = gsub(pattern = "fk_*", "chr", zoox$V1)
zoox$V2 = gsub(".trim.*", "", zoox$V2)
zoox = zoox %>% filter(zoox$V1 != "*")
zooxLst = split(zoox$V2, as.integer(gl(length(zoox$V2), 20, length(zoox$V2))))
zooxMaps = NULL
for(i in zooxLst){
zooxMaps = rbind(zooxMaps, data.frame(t(i)))
}
# remove tech reps
zooxMaps = zooxMaps[-c(66, 68, 164, 166, 209, 211),]
# rename columns and samples to match other ITS2 dataframe
zooxMaps$X1 = gsub("fk_S", "SFK", zooxMaps$X1)
zooxMaps$X1 = gsub("\\.[1-3]", "", zooxMaps$X1)
colnames(zooxMaps) = c("sample",zoox$V1[c(2:20)])
# convert characters to numeric
str(zooxMaps)## 'data.frame': 220 obs. of 20 variables:
## $ sample: chr "SFK001" "SFK002" "SFK003" "SFK004" ...
## $ chr1 : chr "77" "59" "37" "953" ...
## $ chr2 : chr "78" "82" "22" "1069" ...
## $ chr3 : chr "87" "79" "30" "1383" ...
## $ chr4 : chr "80" "118" "19" "1360" ...
## $ chr5 : chr "2" "2" "0" "18" ...
## $ chr6 : chr "24" "17" "40" "18" ...
## $ chr7 : chr "42" "67" "35" "13" ...
## $ chr8 : chr "63" "85" "57" "29" ...
## $ chr9 : chr "61" "58" "51" "30" ...
## $ chr10 : chr "3065" "3920" "3611" "2820" ...
## $ chr11 : chr "4388" "5206" "5077" "3605" ...
## $ chr12 : chr "4606" "5437" "5442" "3855" ...
## $ chr13 : chr "4294" "4919" "4995" "3485" ...
## $ chr14 : chr "3538" "4217" "4013" "3072" ...
## $ chr15 : chr "518" "566" "533" "437" ...
## $ chr16 : chr "29" "105" "13" "82" ...
## $ chr17 : chr "19" "59" "17" "28" ...
## $ chr18 : chr "13" "44" "4" "26" ...
## $ chr19 : chr "3" "6" "1" "5" ...for(i in c(2:20)){
zooxMaps[,i] = as.numeric(zooxMaps[,i])
}
str(zooxMaps)## 'data.frame': 220 obs. of 20 variables:
## $ sample: chr "SFK001" "SFK002" "SFK003" "SFK004" ...
## $ chr1 : num 77 59 37 953 44 ...
## $ chr2 : num 78 82 22 1069 68 ...
## $ chr3 : num 87 79 30 1383 80 ...
## $ chr4 : num 80 118 19 1360 53 ...
## $ chr5 : num 2 2 0 18 4 105 0 0 0 0 ...
## $ chr6 : num 24 17 40 18 9 34 19 26 43 20 ...
## $ chr7 : num 42 67 35 13 22 35 21 57 71 50 ...
## $ chr8 : num 63 85 57 29 32 33 40 60 80 86 ...
## $ chr9 : num 61 58 51 30 39 32 30 84 72 43 ...
## $ chr10 : num 3065 3920 3611 2820 1501 ...
## $ chr11 : num 4388 5206 5077 3605 1807 ...
## $ chr12 : num 4606 5437 5442 3855 2061 ...
## $ chr13 : num 4294 4919 4995 3485 1736 ...
## $ chr14 : num 3538 4217 4013 3072 1564 ...
## $ chr15 : num 518 566 533 437 250 190 473 769 559 821 ...
## $ chr16 : num 29 105 13 82 71 93 20 11 59 19 ...
## $ chr17 : num 19 59 17 28 24 51 22 40 40 38 ...
## $ chr18 : num 13 44 4 26 40 55 16 16 33 13 ...
## $ chr19 : num 3 6 1 5 2 13 3 0 0 7 ...# collapse fake chromosomes into representative genera
zooxMaps$Symbiodinium = rowSums(zooxMaps[2:6])
zooxMaps$Breviolum = rowSums(zooxMaps[7:10])
zooxMaps$Cladocopium = rowSums(zooxMaps[11:16])
zooxMaps$Durusdinium = rowSums(zooxMaps[17:20])
# keep genera totals and turn into proportions for barplot
zooxMaps = zooxMaps[,c(1, 21:24)]
zooxProp = zooxMaps
zooxProp$sum = apply(zooxProp[, c(2:length(zooxProp[1,]))], 1, function(x) {
sum(x, na.rm = T)
})
zooxProp = cbind(zooxProp$sample, (zooxProp[, c(2:(ncol(zooxProp)-1))]
/ zooxProp$sum))
colnames(zooxProp)[1] = "sample"
head(zooxProp)## sample Symbiodinium Breviolum Cladocopium Durusdinium
## 1 SFK001 0.015438128 0.009053223 0.9724591 0.003049507
## 2 SFK002 0.013575022 0.009063323 0.9688174 0.008544279
## 3 SFK003 0.004500563 0.007625953 0.9864150 0.001458516
## 4 SFK004 0.214599785 0.004038047 0.7750359 0.006326274
## 5 SFK005 0.026469650 0.010842989 0.9481237 0.014563623
## 6 SFK006 0.718347383 0.005328456 0.2678941 0.008430094# Check that all samples total to 1
apply(zooxProp[, c(2:(ncol(zooxProp)))], 1, function(x) {
sum(x, na.rm = T)
})## 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 67
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 157 158 159 160 161 162 163 165 167 168 169 170 171 172 173 174 175 176 177 178 179 180
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 203 204 205 206 207 208 210 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1# add sample metadata to proportions
snpSym = popData %>% left_join(zooxProp)## Joining with `by = join_by(sample)`Combining SNP and ITS2 data for comparison of Symbiodiniaceae genera This will allow us to plot individuals in the same order across methods
#sum profiles into genera
symGenera = its2Profs
symGenera$itsSymbiodinium = rowSums(symGenera[6:11])
symGenera$itsBreviolum = rowSums(symGenera[12:14])
symGenera$itsCladocopium = rowSums(symGenera[15:24])
symGenera$itsDurusdinium = 0
symGenera = symGenera %>% dplyr::select(sample, barPlotOrder, itsSymbiodinium, itsBreviolum, itsCladocopium, itsDurusdinium) %>% left_join(admixOrd) %>% relocate(ord, .after = barPlotOrder)## Joining with `by = join_by(sample)`#convert to proportions
symGenera$sum = apply(symGenera[, c(4:length(symGenera[1,]))], 1, function(x) {
sum(x, na.rm = T)
})
symGeneraProp = cbind(symGenera$sample, symGenera[, c(4:(ncol(symGenera)-1))]
/ symGenera$sum)
colnames(symGeneraProp)[1] = "sample"
#Check that all samples total to 1
apply(symGeneraProp[,c(2:5)], 1, function(x) {
sum(x, na.rm = T)
})## [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [42] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [83] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [124] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [165] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [206] 1 1 1 1 1 1 1 1 1 1 1 1 1 1#construct combined dataframe
symGenera = symGenera %>% dplyr::select(sample, ord) %>% left_join(snpSym) %>% left_join(symGeneraProp) ## Joining with `by = join_by(sample)`
## Joining with `by = join_by(sample)`symGenera$depth = factor(symGenera$depth)
symGenera$depth = factor(symGenera$depth, levels = levels(symGenera$depth)[c(2, 1)])
symGenera$site = factor(symGenera$site)
symGenera$site = factor(symGenera$site, levels = levels(symGenera$site)[c(2, 3, 1, 4)])
#turn into melted dataframe with otustack() and remove "summ" rows
gssSym = otuStack(symGenera, count.columns = c(5:length(symGenera[1, ])),
condition.columns = c(1:4)) %>% filter(otu != "summ") %>% droplevels()
#check that levels are correct/ordered
levels(gssSym$otu)## [1] "Symbiodinium" "Breviolum" "Cladocopium" "Durusdinium"
## [5] "itsSymbiodinium" "itsBreviolum" "itsCladocopium" "itsDurusdinium"levels(gssSym$depth)## [1] "Shallow" "Mesophotic"levels(gssSym$site)## [1] "Riley's Hump" "Tortugas Bank" "Lower Keys" "Upper Keys"Creating Symbiodiniaceae genera relative proportion barplots
SNPs:
gssSymPlot = gssSym %>% left_join(zooxAnno, by = "site") %>% left_join(pcangsdITS)## Joining with `by = join_by(sample)`gssSymPlot$ord = as.numeric(gssSymPlot$ord)
zooxSNPA = ggplot(data = subset(gssSymPlot, subset = otu %in% c("Symbiodinium", "Breviolum", "Cladocopium", "Durusdinium" )), aes(x = ord, y = count, fill = otu, order = ord)) +
geom_point(aes(x=1, y=0.5, fill = otu), shape = 22, size = 0) +
geom_bar(stat = "identity", position = "stack", colour = "grey25", width = 1, size = 0.2, show.legend = FALSE) +
xlab("Population") +
scale_fill_manual(values = colPalZoox, name = "Symbiodiniaceae genus") +
geom_segment(data = (subset(gssSymPlot, subset = otu %in% c("Symbiodinium", "Breviolum", "Cladocopium", "Durusdinium" )) %>% filter(depth == "Mesophotic")), aes(x = x1, xend = x2, y = y1, yend = y2, color = site), size = 7) +
scale_color_manual(values = rev(flPal), guide = "none") +
ggnewscale::new_scale_color() +
geom_segment(data = (subset(gssSymPlot, subset = otu %in% c("Symbiodinium", "Breviolum", "Cladocopium", "Durusdinium" ))), aes(x = ord-0.5, xend = ord+0.5, color = cluster), y = -.07, yend = -.07, linewidth = 4) +
scale_color_manual(values = kColPal, name = "Lineage") +
coord_cartesian(ylim = c(0, 1), xlim = c(0.5, 30.5), clip = "off") +
scale_x_discrete(expand = c(0.005, 0.005)) +
scale_y_continuous(expand = c(0.001, 0.001)) +
facet_grid(factor(depth) ~ site, drop = TRUE, scales = "free", switch = "both", space = "free") +
geom_text(data = subset(gssSymPlot, subset = otu == "Symbiodinium") %>% filter(sample %in% c("SFK095", "SFK015")), x = 15.5, y = -.205, aes(label = site), size = 4.4, color = "#FFFFFF") +
geom_text(data = subset(gssSymPlot, subset = otu == "Symbiodinium") %>% filter(sample %in% c("SFK195", "SFK156")), x = 15.5, y = -.205, aes(label = site), size = 4.4, color = "#000000") +
ggtitle("2bRAD") +
guides(color = guide_legend(override.aes = list(size = 4), ncol = 1), fill = "none") +
theme_bw()
zooxSNP = zooxSNPA + theme(plot.title = element_text(),
panel.grid = element_blank(),
# panel.background = element_blank(),
panel.background = element_rect(fill = "gray70"),
panel.border = element_rect(fill = NA, color = "black", size = 0.75, linetype = "solid"),
panel.spacing.x = grid:::unit(0.05, "lines"),
panel.spacing.y = grid:::unit(0.82, "lines"),
axis.text.x = element_blank(),
axis.text.y = element_blank(),
axis.ticks.x = element_blank(),
axis.ticks.y = element_blank(),
axis.title = element_blank(),
strip.background.x = element_blank(),
strip.background.y = element_blank(),
strip.text = element_text(size = 12),
strip.text.y.left = element_text(size = 12, angle = 90),
strip.text.x.bottom = element_text(vjust = -.1, color = NA),
legend.title = element_text(size = 10),
legend.text = element_text(size = 8),
legend.key.size = unit(0.75, "line"),
legend.key = element_blank(),
legend.position = "bottom",
legend.direction = "vertical",
legend.box = "horizontal")
# zooxSNPITS2:
zooxITSA = ggplot(data = subset(gssSymPlot, subset = !(otu %in% c("Symbiodinium", "Breviolum", "Cladocopium", "Durusdinium" ))), aes(x = ord, y = count, fill = otu, order = ord)) +
geom_point(aes(x=1, y=0.5, fill = otu), shape = 22, size = 0) +
geom_bar(stat = "identity", position = "stack", colour = "grey25", width = 1, size = 0.2, show.legend = FALSE) +
xlab("Population") +
scale_fill_manual(values = colPalZoox, name = "Symbiodiniaceae genus", labels = c("Symbiodinium", "Breviolum", "Cladocopium", "Durusdinium")) +
geom_segment(data = (subset(gssSymPlot, subset = !otu %in% c("Symbiodinium", "Breviolum", "Cladocopium", "Durusdinium" ))), aes(x = ord-0.5, xend = ord+0.5, color = cluster), y = -.07, yend = -.07, linewidth = 4) +
scale_color_manual(values = kColPal, guide = "none") +
ggnewscale::new_scale_color() +
geom_segment(data = (subset(gssSymPlot, subset = !otu %in% c("Symbiodinium", "Breviolum", "Cladocopium", "Durusdinium" )) %>% filter(depth == "Mesophotic")), aes(x = x1, xend = x2, y = y1, yend = y2, color = site), size = 7) +
scale_color_manual(values = rev(flPal)) +
coord_cartesian(ylim = c(0, 1), xlim = c(0.5, 30.5), clip = "off") +
scale_x_discrete(expand = c(0.005, 0.005)) +
scale_y_continuous(expand = c(0.001, 0.001)) +
facet_grid(factor(depth) ~ site, drop = TRUE, scales = "free", switch = "both", space = "free") +
geom_text(data = subset(gssSymPlot, subset = otu == "itsSymbiodinium") %>% filter(sample %in% c("SFK095", "SFK015")), x = 15.5, y = -.205, aes(label = site), size = 4.4, color = "#FFFFFF") +
geom_text(data = subset(gssSymPlot, subset = otu == "itsSymbiodinium") %>% filter(sample %in% c("SFK195", "SFK156")), x = 15.5, y = -.205, aes(label = site), size = 4.4, color = "#000000") +
guides(fill = guide_legend(override.aes = list(size = 4), ncol = 1), color = "none") +
labs(title = expression(italic("ITS2"))) +
theme_bw()
zooxITS = zooxITSA + theme(plot.title = element_text(),
panel.grid = element_blank(),
# panel.background = element_blank(),
panel.background = element_rect(fill = "gray70"),
panel.border = element_rect(fill = NA, color = "black", size = 0.75, linetype = "solid"),
panel.spacing.x = grid:::unit(0.05, "lines"),
panel.spacing.y = grid:::unit(0.82, "lines"),
axis.text.x = element_blank(),
axis.text.y = element_blank(),
axis.ticks.x = element_blank(),
axis.ticks.y = element_blank(),
axis.title = element_blank(),
strip.background.x = element_blank(),
strip.background.y = element_blank(),
strip.text = element_text(size = 12),
strip.text.y.left = element_text(size = 12, angle = 90),
strip.text.x.bottom = element_text(vjust = -.1, color = NA),
legend.title = element_text(size = 10),
legend.text = element_text(size = 8, face = "italic"),
legend.key = element_blank(),
legend.key.size = unit(0.5, "line"),
legend.position = "bottom",
legend.direction = "vertical",
legend.box = "horizontal")
# zooxITSSymbiodiniaceae barplots
# its2Plots = (its2ProfsPlot + theme(legend.position = "right", legend.title = element_text(angle = 0)) & guides(color = "none", fill = guide_legend(ncol = 1, reverse = FALSE)))/zooxITS/zooxSNP +
its2Plots = zooxITS/zooxSNP +
plot_annotation(tag_levels = "A") &
theme(plot.tag = element_text(size = 16),
axis.text = element_blank(),
axis.ticks = element_blank(),
legend.position = "right",
legend.title = element_text(color = "black", size = 10),
legend.text = element_text(color = "black", size = 8))
ggsave("../figures/figure6.png", plot = its2Plots, height = 18, width = 20, units = "cm", dpi = 300)
ggsave("../figures/figure6.svg", plot = its2Plots, height = 18, width = 20, units = "cm", dpi = 300)Procrustes analysis
Comparing the two outputs with procrustes analysis
popData = read.csv("../data/stephanocoeniaMetaData.csv")[-c(66, 68, 164, 166, 209, 211),] %>% dplyr::select("sample" = tubeID, "site" = site, "depth" = depthZone)
symSnpDf = zooxMaps %>% left_join(popData) %>% relocate(c(site, depth), .after = sample) %>% filter(!row_number()==131) %>% mutate(dataSet = "SNPs") %>% relocate(dataSet, .after = sample)## Joining with `by = join_by(sample)`rownames(symSnpDf) = symSnpDf$sample
symITS2 = its2Profs
symITS2$Symbiodinium = rowSums(symITS2[6:11])
symITS2$Breviolum = rowSums(symITS2[12:14])
symITS2$Cladocopium = rowSums(symITS2[15:24])
symITS2$Durusdinium = 0
symITS2Df = symITS2 %>% dplyr::select(sample, Symbiodinium, Breviolum, Cladocopium, Durusdinium) %>% left_join(popData) %>% relocate(c(site, depth), .after = sample) %>% arrange(sample) %>% mutate(dataSet = "ITS2") %>% relocate(dataSet, .after = sample)## Joining with `by = join_by(sample)`rownames(symITS2Df) = symITS2Df$sample
#create distance matrices
symSnpdist = vegdist(decostand(symSnpDf[c(5:ncol(symSnpDf))], method = "normalize"), method = "bray")
symITS2dist = vegdist(decostand(symITS2Df[c(5:ncol(symITS2Df))], method = "normalize"), method = "bray")
snpPcOA = cmdscale(symSnpdist, eig = TRUE, x.ret = TRUE)
its2PcOA = cmdscale(symITS2dist, eig = TRUE, x.ret = TRUE)
#procrustes analysis
its2GeneraProcrustes = protest(Y = its2PcOA, X = snpPcOA, choices = c(1, 2),
permutations = 9999, symmetric = FALSE)
its2GeneraProcrustes##
## Call:
## protest(X = snpPcOA, Y = its2PcOA, permutations = 9999, choices = c(1, 2), symmetric = FALSE)
##
## Procrustes Sum of Squares (m12 squared): 0.1561
## Correlation in a symmetric Procrustes rotation: 0.9187
## Significance: 0.0001
##
## Permutation: free
## Number of permutations: 9999plot(its2GeneraProcrustes, kind = 1)
plot(its2GeneraProcrustes, kind = 2)
popData = read.csv("../data/stephanocoeniaMetaData.csv")[-c(66, 68, 164, 166, 209, 211),] %>% dplyr::select("sample" = tubeID, "site" = site, "depth" = depthZone)
symGenProcPlot = procrustes(X = snpPcOA, Y = its2PcOA, choices = c(1, 2), symmetric = FALSE)
symGenProcPlotData = cbind(symGenProcPlot$X, symGenProcPlot$Yrot) %>% as.data.frame()
rownames(symGenProcPlotData) = rownames(symGenProcPlot$X)
colnames(symGenProcPlotData) = c("x1", "y1", "x2", "y2")
symGenProcPlotData$sample = row.names(symGenProcPlotData)
symGenProcPlotData$sample = gsub(pattern = "\\.2", "", symGenProcPlotData$sample)
symGenProcPlotData = symGenProcPlotData %>% left_join(popData) %>% relocate(sample, .before = x1)## Joining with `by = join_by(sample)`symGenProcPlotData$depth = factor(symGenProcPlotData$depth)
symGenProcPlotData$depth = factor(symGenProcPlotData$depth, levels(symGenProcPlotData$depth)[c(2,1)])
symGenProcPlotData$site = factor(symGenProcPlotData$site)
symGenProcPlotData$site = factor(symGenProcPlotData$site, levels(symGenProcPlotData$site)[c(4, 1, 3, 2)])
head(symGenProcPlotData)## sample x1 y1 x2 y2 site depth
## 1 SFK001 -0.12000280 0.0086944453 -0.1479462 -0.0009289205 Tortugas Bank Mesophotic
## 2 SFK002 -0.11999040 0.0070304820 -0.1310021 0.0069938380 Tortugas Bank Mesophotic
## 3 SFK003 -0.13226702 0.0005281082 -0.1479462 -0.0009289205 Tortugas Bank Mesophotic
## 4 SFK004 0.06906114 -0.0593680913 0.1380109 -0.0344824197 Tortugas Bank Mesophotic
## 5 SFK005 -0.10683940 0.0156151506 -0.1479462 -0.0009289205 Tortugas Bank Mesophotic
## 6 SFK006 0.57063159 -0.0790486422 0.4430768 -0.0616296036 Tortugas Bank Mesophotic#Calculate the angle of rotation, and then the slope of the rotated axis
theta = acos(symGenProcPlot$rotation[1,1])
slope = tan(theta)
#Calculate the y-intercepts for rotated axes
symGenProcInt = symGenProcPlot$translation[2] - (slope*symGenProcPlot$translation[1])
symGenProcInt2 = symGenProcPlot$translation[2] - (-1/slope*symGenProcPlot$translation[1])
sintSymGenProcPlotA = ggplot() +
geom_hline(yintercept = 0, color = "gray90", linetype = 1) +
geom_vline(xintercept = 0, color = "gray90", linetype = 1) +
geom_abline(intercept = symGenProcInt, slope = slope, color = "gray75", linetype = 2) +
geom_abline(intercept = symGenProcInt2, slope = -(1/slope), color = "gray75", linetype = 2) +
geom_segment(data = symGenProcPlotData, aes(x = x2*(1-symGenProcPlot$scale), y = y2*(1-symGenProcPlot$scale), xend = x1*(1-symGenProcPlot$scale), yend = y1*(1-symGenProcPlot$scale), color = site), alpha = 0.5) +
geom_point(data = symGenProcPlotData, aes(x = x2*(1-symGenProcPlot$scale), y = y2*(1-symGenProcPlot$scale), fill = site, shape = depth), alpha = 0.5)+
geom_point(data = symGenProcPlotData, aes(x = x1*(1-symGenProcPlot$scale), y = y1*(1-symGenProcPlot$scale), fill = site, shape = depth), size = 2) +
annotate(geom = "label", x = 0.172, y = 0.172, label = " ", size = 10) +
annotate(geom = "text", x = 0.172, y = 0.1825, label = "Procrustes analysis:", size = 3) +
annotate(geom = "text", x = 0.172, y = 0.165, label = "italic(t[0]) == 0.919 *','~italic(p) < 0.0001", parse = TRUE, size = 3) +
scale_color_manual(values = flPal) +
scale_fill_manual(values = flPal, name = "Site") +
scale_shape_manual(values = c(21, 23), name = "Depth zone") +
guides(color = "none", fill = guide_legend(override.aes = list(shape = 15, color = flPal, size = 3), ncol =2, order = 1), shape = guide_legend(ncol = 1)) +
theme_bw()
sintSymGenProcPlot = sintSymGenProcPlotA +
theme(panel.grid = element_blank(),
panel.border = element_rect(color = "black", size = 0.75, fill = NA),
axis.title = element_blank(),
axis.ticks = element_line(color = "black"),
axis.text = element_text(color = "black", size = 8),
legend.position = "bottom",
legend.direction = "vertical",
legend.box = "horizontal",
legend.key = element_blank(),
legend.background = element_blank(),
legend.title = element_text(color = "black", size = 8),
legend.text = element_text(color = "black", size = 8)
)
sintSymGenProcPlot
S. intersepta genetic distance vs Symbiodiniaceae B-C distance
its2DistA = its2Profs %>% arrange(sample)
its2Dist = its2DistA[c(6:ncol(its2Profs))] %>% decostand("normalize") %>% vegdist(method = "bray")
its2PCoA = cmdscale(its2Dist, eig = TRUE, x.ret = TRUE)
sintIBS = read.table("../data/snps/sintFiltSnps.ibsMat")[-131,-131] %>% as.matrix() %>% as.dist(diag = FALSE)
sintPCoA = cmdscale(sintIBS, eig = TRUE, x.ret = TRUE)
set.seed(981)
its2IBSProcrustes = protest(X = sintPCoA, Y = its2PCoA, permutations = 9999)
its2IBSProcrustes##
## Call:
## protest(X = sintPCoA, Y = its2PCoA, permutations = 9999)
##
## Procrustes Sum of Squares (m12 squared): 0.9519
## Correlation in a symmetric Procrustes rotation: 0.2194
## Significance: 0.0001
##
## Permutation: free
## Number of permutations: 9999plot(its2IBSProcrustes)
plot(its2IBSProcrustes, kind = 2)
admixpops = read.csv("../data/stephanocoeniaMetaData.csv")[-c(66, 68, 164, 166, 209, 211),] %>% dplyr::select("sample" = tubeID, "pop" = site, "depth" = depthZone)
admixpops$popdepth = as.factor(paste(admixpops$pop, admixpops$depth, sep = " "))
clumpp4 = read.table("../data/snps/k/ClumppK4.output", header = FALSE)
clumpp4$V1 = admixpops$sample
sintAdmix = admixpops[-131,] %>% left_join(clumpp4[,c(1, 6:9)], by = c("sample" = "V1"))
admixDist = sintAdmix[c(5:ncol(sintAdmix))] %>% vegdist(method = "euclidean")
admixPCoA = cmdscale(admixDist, eig = TRUE, x.ret = TRUE)
set.seed(981)
its2AdmixProcrustes = protest(X = admixPCoA, Y = its2PCoA, permutations = 9999)
its2AdmixProcrustes##
## Call:
## protest(X = admixPCoA, Y = its2PCoA, permutations = 9999)
##
## Procrustes Sum of Squares (m12 squared): 0.9359
## Correlation in a symmetric Procrustes rotation: 0.2531
## Significance: 0.0001
##
## Permutation: free
## Number of permutations: 9999plot(its2AdmixProcrustes, kind = 1)
plot(its2AdmixProcrustes, kind = 2)
Cheking dispersion with PERMDISP
Using vegan::betadisper() to look at multivariate
homogeneity of dispersion (PERMDISP) between sites and depths. This is
using Bray-Curtis dissimilarity.
alpha = with(its2Profs, tapply(specnumber(its2Profs[, c(6:ncol(its2Profs))]), site, mean))
alpha## Riley's Hump Tortugas Bank Lower Keys Upper Keys
## 1.177778 1.290909 1.237288 1.200000gamma = with(its2Profs, specnumber(its2Profs[, c(6:ncol(its2Profs))], site))
gamma## Riley's Hump Tortugas Bank Lower Keys Upper Keys
## 13 11 11 9gamma/alpha## Riley's Hump Tortugas Bank Lower Keys Upper Keys
## 11.037736 8.521127 8.890411 7.500000set.seed(694)
its2dispS = betadisper(vegdist(decostand(its2Profs[, c(6:ncol(its2Profs))], "normalize")), its2Profs$site)
set.seed(694)
permutest(its2dispS, permutations = 9999)##
## Permutation test for homogeneity of multivariate dispersions
## Permutation: free
## Number of permutations: 9999
##
## Response: Distances
## Df Sum Sq Mean Sq F N.Perm Pr(>F)
## Groups 3 0.0892 0.029731 0.7719 9999 0.516
## Residuals 215 8.2815 0.038518No significant effect of Site on betadiversity.
alpha = with(its2Profs, tapply(specnumber(its2Profs[, c(6:ncol(its2Profs))]), depthZone, mean))
alpha## Shallow Mesophotic
## 1.310924 1.130000gamma = with(its2Profs, specnumber(its2Profs[, c(6:ncol(its2Profs))], depthZone))
gamma## Shallow Mesophotic
## 18 11gamma/alpha## Shallow Mesophotic
## 13.730769 9.734513set.seed(694)
its2dispD = betadisper(vegdist(decostand(its2Profs[, c(6:ncol(its2Profs))], "normalize")), its2Profs$depthZone)
its2dispD##
## Homogeneity of multivariate dispersions
##
## Call: betadisper(d = vegdist(decostand(its2Profs[, c(6:ncol(its2Profs))],
## "normalize")), group = its2Profs$depthZone)
##
## No. of Positive Eigenvalues: 28
## No. of Negative Eigenvalues: 27
##
## Average distance to median:
## Shallow Mesophotic
## 0.6525 0.5770
##
## Eigenvalues for PCoA axes:
## (Showing 8 of 55 eigenvalues)
## PCoA1 PCoA2 PCoA3 PCoA4 PCoA5 PCoA6 PCoA7 PCoA8
## 26.118 18.773 13.961 9.281 6.898 6.102 3.901 3.190set.seed(694)
its2dispDPerm = permutest(its2dispD, permutations = 9999)
its2dispDPerm##
## Permutation test for homogeneity of multivariate dispersions
## Permutation: free
## Number of permutations: 9999
##
## Response: Distances
## Df Sum Sq Mean Sq F N.Perm Pr(>F)
## Groups 1 0.3104 0.310449 10.815 9999 0.0013 **
## Residuals 217 6.2290 0.028705
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Depth does significantly affect beta diversity.
Running PERMANOVA in R
Now let’s see how different communities are from each other with
PERMANOVA. We will utilize the vegan::adonis() function. We
will use Bray-Curtis similarity for our distance matrix and run a total
0f 9,999 permutations, and test the effects of Site, Depth, and the
interaction between Site and Depth.
set.seed(694)
its2Adonis = adonis2(decostand(its2Profs[, c(6:ncol(its2Profs))], "normalize") ~ site * depthZone, data = its2Profs, permutations = 9999, method = "bray")
its2Adonis ## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 9999
##
## adonis2(formula = decostand(its2Profs[, c(6:ncol(its2Profs))], "normalize") ~ site * depthZone, data = its2Profs, permutations = 9999, method = "bray")
## Df SumOfSqs R2 F Pr(>F)
## site 3 8.455 0.09229 8.4735 0.0001 ***
## depthZone 1 5.368 0.05860 16.1410 0.0001 ***
## site:depthZone 3 7.612 0.08309 7.6290 0.0001 ***
## Residual 211 70.178 0.76602
## Total 218 91.613 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1set.seed(694)
its2PWAdonis = pairwise.adonis(decostand(its2Profs[,c(6:ncol(its2Profs))], "normalize"), factors = its2Profs$site, sim.method = "bray", p.adjust.m = "fdr", perm = 9999)
its2PWAdonis## pairs Df SumsOfSqs F.Model R2 p.value p.adjusted
## 1 Riley's Hump vs Tortugas Bank 1 3.0054485 7.606522 0.07202701 0.0001 0.00015
## 2 Riley's Hump vs Lower Keys 1 4.5770266 11.695628 0.10286788 0.0001 0.00015
## 3 Riley's Hump vs Upper Keys 1 3.4373577 9.258215 0.08247250 0.0001 0.00015
## 4 Tortugas Bank vs Lower Keys 1 0.9811441 2.446466 0.02137651 0.0334 0.03340
## 5 Tortugas Bank vs Upper Keys 1 1.6940679 4.427003 0.03770004 0.0014 0.00168
## 6 Lower Keys vs Upper Keys 1 3.4239083 9.014882 0.07153823 0.0001 0.00015
## sig
## 1 **
## 2 **
## 3 **
## 4 .
## 5 *
## 6 **its2profsRxD = paste(its2Profs$site, its2Profs$depthZone, sep = " ")
set.seed(694)
its2PWAdonis2 = pairwise.adonis(decostand(its2Profs[, c(6:ncol(its2Profs))], "normalize"), factors = its2profsRxD, sim.method = "bray", p.adjust.m = "fdr", perm = 9999)
its2PWAdonis2## pairs Df SumsOfSqs F.Model
## 1 Riley's Hump Shallow vs Riley's Hump Mesophotic 1 0.6958548 1.851042
## 2 Riley's Hump Shallow vs Tortugas Bank Shallow 1 2.8030339 7.429995
## 3 Riley's Hump Shallow vs Tortugas Bank Mesophotic 1 3.0884300 8.640125
## 4 Riley's Hump Shallow vs Lower Keys Shallow 1 5.0368955 15.488866
## 5 Riley's Hump Shallow vs Lower Keys Mesophotic 1 4.6194798 13.467087
## 6 Riley's Hump Shallow vs Upper Keys Shallow 1 4.7577989 13.584574
## 7 Riley's Hump Shallow vs Upper Keys Mesophotic 1 2.2928012 6.845341
## 8 Riley's Hump Mesophotic vs Tortugas Bank Shallow 1 1.7078494 4.482170
## 9 Riley's Hump Mesophotic vs Tortugas Bank Mesophotic 1 1.1310176 3.195846
## 10 Riley's Hump Mesophotic vs Lower Keys Shallow 1 3.2155336 10.357235
## 11 Riley's Hump Mesophotic vs Lower Keys Mesophotic 1 1.8698060 5.584033
## 12 Riley's Hump Mesophotic vs Upper Keys Shallow 1 2.4146387 7.007461
## 13 Riley's Hump Mesophotic vs Upper Keys Mesophotic 1 0.4347921 1.342139
## 14 Tortugas Bank Shallow vs Tortugas Bank Mesophotic 1 2.6960612 7.456037
## 15 Tortugas Bank Shallow vs Lower Keys Shallow 1 1.9880105 6.041735
## 16 Tortugas Bank Shallow vs Lower Keys Mesophotic 1 3.3741943 9.729362
## 17 Tortugas Bank Shallow vs Upper Keys Shallow 1 3.5069150 9.905960
## 18 Tortugas Bank Shallow vs Upper Keys Mesophotic 1 3.2100015 9.476618
## 19 Tortugas Bank Mesophotic vs Lower Keys Shallow 1 5.1052811 16.781413
## 20 Tortugas Bank Mesophotic vs Lower Keys Mesophotic 1 0.3865517 1.192600
## 21 Tortugas Bank Mesophotic vs Upper Keys Shallow 1 1.2421221 3.741093
## 22 Tortugas Bank Mesophotic vs Upper Keys Mesophotic 1 1.2155556 3.855401
## 23 Lower Keys Shallow vs Lower Keys Mesophotic 1 6.2867594 21.368532
## 24 Lower Keys Shallow vs Upper Keys Shallow 1 6.0655965 20.114834
## 25 Lower Keys Shallow vs Upper Keys Mesophotic 1 6.1026797 21.338933
## 26 Lower Keys Mesophotic vs Upper Keys Shallow 1 1.5730255 4.919063
## 27 Lower Keys Mesophotic vs Upper Keys Mesophotic 1 2.7859051 9.149432
## 28 Upper Keys Shallow vs Upper Keys Mesophotic 1 3.3019234 10.593110
## R2 p.value p.adjusted sig
## 1 0.04127087 0.1058 0.1139384615
## 2 0.11355640 0.0001 0.0002000000 **
## 3 0.14017047 0.0001 0.0002000000 **
## 4 0.21367234 0.0001 0.0002000000 **
## 5 0.18843760 0.0001 0.0002000000 **
## 6 0.18976958 0.0001 0.0002000000 **
## 7 0.10556412 0.0003 0.0004941176 **
## 8 0.09439691 0.0008 0.0011200000 *
## 9 0.07757691 0.0243 0.0272160000 .
## 10 0.19781860 0.0001 0.0002000000 **
## 11 0.11493555 0.0025 0.0033333333 *
## 12 0.14012831 0.0003 0.0004941176 **
## 13 0.03026780 0.2359 0.2446370370
## 14 0.12332989 0.0002 0.0003733333 **
## 15 0.09583706 0.0008 0.0011200000 *
## 16 0.14365058 0.0001 0.0002000000 **
## 17 0.14587762 0.0001 0.0002000000 **
## 18 0.14044299 0.0001 0.0002000000 **
## 19 0.24398180 0.0001 0.0002000000 **
## 20 0.02200670 0.2878 0.2878000000
## 21 0.06593269 0.0121 0.0147304348 .
## 22 0.06781063 0.0221 0.0257833333 .
## 23 0.27266725 0.0001 0.0002000000 **
## 24 0.26084260 0.0001 0.0002000000 **
## 25 0.27239244 0.0001 0.0002000000 **
## 26 0.07818081 0.0047 0.0059818182 *
## 27 0.13625479 0.0004 0.0006222222 **
## 28 0.15443402 0.0001 0.0002000000 **its2PWAdonis2 %>% filter(p.adjusted > 0.05)## pairs Df SumsOfSqs F.Model R2
## 1 Riley's Hump Shallow vs Riley's Hump Mesophotic 1 0.6958548 1.851042 0.04127087
## 2 Riley's Hump Mesophotic vs Upper Keys Mesophotic 1 0.4347921 1.342139 0.03026780
## 3 Tortugas Bank Mesophotic vs Lower Keys Mesophotic 1 0.3865517 1.192600 0.02200670
## p.value p.adjusted sig
## 1 0.1058 0.1139385
## 2 0.2359 0.2446370
## 3 0.2878 0.2878000its2PWAdonis2Tab = its2PWAdonis2 %>% mutate(pairs = pairs, F.Model = round(F.Model, 3), R2 = round(R2,3), p.adjusted = round(p.adjusted, 4)) %>%dplyr::select(-p.value, -sig, -SumsOfSqs, -Df) %>%
flextable() %>%
flextable::compose(part = "header", j = "pairs", value = as_paragraph("Comparison")) %>%
flextable::compose(part = "header", j = "F.Model", value = as_paragraph(as_i("Pseudo-F"))) %>%
flextable::compose(part = "header", j = "R2", value = as_paragraph(as_i("R2"))) %>%
flextable::compose(part = "header", j = "p.adjusted", value = as_paragraph("p-value")) %>%
flextable::font(fontname = "Times New Roman", part = "all") %>%
flextable::fontsize(size = 10, part = "all") %>%
flextable::bold(part = "header") %>%
flextable::align(align = "left", part = "all") %>%
flextable::autofit()
table4 = read_docx()
table4 = body_add_flextable(table4, value = its2PWAdonis2Tab)
print(table4, target = "../tables/table4.docx")Symbiodiniaceae dbRDA
options(sdmpredictors_datadir="../data/snps/bioOracle", timeout = max(300, getOption("timeout")))
popData = read.csv("../data/stephanocoeniaMetaData.csv")[-c(66, 68, 164, 166, 209, 211),] %>% dplyr::select(sample, site, depthZone, latDD, longDD, depthM)
its2DistA = its2Profs %>% dplyr::arrange(sample)
its2Dist = vegdist(decostand(its2DistA[, c(6:ncol(its2DistA))], "normalize"), method = "bray")
datasets = list_datasets(terrestrial = FALSE, marine = TRUE, freshwater = FALSE)
# Extract present-day data sets
present = list_layers(datasets) %>% dplyr::select(dataset_code, layer_code, name, units, description, contains("cellsize"), version) %>% filter(version == 22) %>% filter(layer_code %in% c("BO22_damean", "BO22_parmean", "BO22_ph", "BO22_curvelmax_bdmean", "BO22_salinitymean_bdmean", "BO22_salinitymean_ss", "BO22_curvelmean_ss", "BO22_curvelmean_bdmean", "BO22_dissoxmean_bdmean", "BO22_lightbotmax_bdmean", "BO22_lightbotmean_bdmean", "BO22_nitratemean_bdmean", "BO22_tempmax_bdmean", "BO22_tempmean_bdmean", "BO22_ppmean_ss", "BO22_ppmean_bdmean", "BO22_chlomean_ss", "BO22_chlomean_bdmean"))
envVar = load_layers(present$layer_code)
itsEnvData = data.frame(popData, raster::extract(envVar, popData[,5:4]))[-131,] %>% cbind(pcangsd[-131,c(6:8)])
corData = rcorr(as.matrix(itsEnvData[,c(6:ncol(itsEnvData))]), type = "pearson")
corDataFlat = melt(corData$r, value.name = "r")
pDataFlat = melt(corData$P, value.name = "p")
corDataBind = corDataFlat %>% left_join(pDataFlat, by = c("Var1","Var2"))
ggplot(corDataBind) +
geom_tile(aes(x = Var1, y = Var2, fill = r)) +
scale_fill_gradient2(low = "#3B9AB2FF", high = "#F21A00FF") +
geom_text(data = filter(corDataBind, r >= 0.7, p < 0.05),aes(x = Var1, y = Var2, label = round(r, 2))) +
geom_text(data = filter(corDataBind, r <= -0.7, p < 0.05),aes(x = Var1, y = Var2, label = round(r, 2))) +
theme_bw() +
theme(axis.text.x = element_text(angle = 90))
itsEnvData2 = itsEnvData %>% dplyr::select("BO22_curvelmean_ss", "depthM", "BO22_lightbotmean_bdmean", "BO22_tempmean_bdmean", "BO22_ppmean_bdmean", "PC1", "PC2", "PC3")
corData2 = cor(itsEnvData2)
corMelt2 = melt(corData2)
ggplot(corMelt2) +
geom_tile(aes(x = Var1, y = Var2, fill = value)) +
scale_fill_gradient2(low = "#3B9AB2FF", high = "#F21A00FF") +
geom_text(data = corMelt2[corMelt2$value >= 0.7,],aes(x = Var1, y = Var2, label = round(value, 2))) +
geom_text(data = corMelt2[corMelt2$value <= -0.7,],aes(x = Var1, y = Var2, label = round(value, 2))) +
theme_bw() +
theme(axis.text.x = element_text(angle = 90))
vif = diag(solve(cor(itsEnvData2)))
vif## BO22_curvelmean_ss depthM BO22_lightbotmean_bdmean
## 2.077111 1.746855 3.891481
## BO22_tempmean_bdmean BO22_ppmean_bdmean PC1
## 3.432994 3.477058 1.085170
## PC2 PC3
## 1.118312 1.017985itsDbrda0 = dbrda(its2Dist ~ 1, data = itsEnvData2)
itsDbrda1 = dbrda(its2Dist ~ BO22_curvelmean_ss + depthM + BO22_lightbotmean_bdmean + BO22_tempmean_bdmean + BO22_ppmean_bdmean + Condition(PC1, PC2, PC3), data = itsEnvData2)
anova(itsDbrda1)## Permutation test for dbrda under reduced model
## Permutation: free
## Number of permutations: 999
##
## Model: dbrda(formula = its2Dist ~ BO22_curvelmean_ss + depthM + BO22_lightbotmean_bdmean + BO22_tempmean_bdmean + BO22_ppmean_bdmean + Condition(PC1, PC2, PC3), data = itsEnvData2)
## Df SumOfSqs F Pr(>F)
## Model 5 13.179 7.2972 0.001 ***
## Residual 212 76.576
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1RsquareAdj(itsDbrda1)## $r.squared
## [1] 0.1438554
##
## $adj.r.squared
## [1] 0.1247137set.seed(092)
bestItsDbrda <- ordiR2step(itsDbrda0, itsDbrda1, direction = "forward")## Step: R2.adj= 0
## Call: its2Dist ~ 1
##
## R2.adjusted
## <All variables> 0.12471375
## + depthM 0.07556852
## + BO22_curvelmean_ss 0.03225721
## + BO22_tempmean_bdmean 0.02022712
## + BO22_ppmean_bdmean 0.01667414
## + BO22_lightbotmean_bdmean 0.01473131
## <none> 0.00000000
## + Condition(PC1, PC2, PC3) 0.00000000
##
## Df AIC F Pr(>F)
## + depthM 1 974.13 18.821 0.002 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Step: R2.adj= 0.07556852
## Call: its2Dist ~ depthM
##
## R2.adjusted
## <All variables> 0.12471375
## + BO22_curvelmean_ss 0.10492630
## + BO22_lightbotmean_bdmean 0.09605059
## + BO22_ppmean_bdmean 0.09267258
## + BO22_tempmean_bdmean 0.09002396
## <none> 0.07556852
## + Condition(PC1, PC2, PC3) 0.07494747
##
## Df AIC F Pr(>F)
## + BO22_curvelmean_ss 1 968.05 8.1174 0.002 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Step: R2.adj= 0.1049263
## Call: its2Dist ~ depthM + BO22_curvelmean_ss
##
## R2.adjusted
## + BO22_lightbotmean_bdmean 0.1255916
## <All variables> 0.1247137
## + BO22_tempmean_bdmean 0.1210367
## + BO22_ppmean_bdmean 0.1189578
## <none> 0.1049263
## + Condition(PC1, PC2, PC3) 0.1024550bestItsDbrda$anova## R2.adj Df AIC F Pr(>F)
## + depthM 0.075569 1 974.13 18.8206 0.002 **
## + BO22_curvelmean_ss 0.104926 1 968.05 8.1174 0.002 **
## <All variables> 0.124714
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Now create best model and look at variance partitioning and significance with ANOVA
its2Model = itsEnvData2 %>% dplyr::select("Depth" = depthM, "Current" = BO22_curvelmean_ss, PC1, PC2, PC3)
its2Dbrda = dbrda(its2Dist ~ Depth + Current + Condition(PC1, PC2, PC3), its2Model)
itsDbrdaVarPart = varpart(its2Dist, ~ depthM, ~ BO22_curvelmean_ss, data = itsEnvData2)
itsDbrdaDepth = dbrda(its2Dist ~ Depth, its2Model)
itsDbrdaCur = dbrda(its2Dist ~ Current, its2Model)
plot(itsDbrdaVarPart, Xnames = c("Depth", "Current"), bg =c(3:4) , digits = 2)
itsDbrdaVarPart##
## Partition of squared Bray distance in dbRDA
##
## Call: varpart(Y = its2Dist, X = ~depthM, ~BO22_curvelmean_ss, data =
## itsEnvData2)
##
## Explanatory tables:
## X1: ~depthM
## X2: ~BO22_curvelmean_ss
##
## No. of explanatory tables: 2
## Total variation (SS): 91.613
## No. of observations: 219
##
## Partition table:
## Df R.squared Adj.R.squared Testable
## [a+c] = X1 1 0.07981 0.07557 TRUE
## [b+c] = X2 1 0.03670 0.03226 TRUE
## [a+b+c] = X1+X2 2 0.11314 0.10493 TRUE
## Individual fractions
## [a] = X1|X2 1 0.07267 TRUE
## [b] = X2|X1 1 0.02936 TRUE
## [c] 0 0.00290 FALSE
## [d] = Residuals 0.89507 FALSE
## ---
## Use function 'dbrda' to test significance of fractions of interestset.seed(004)
anova(its2Dbrda)## Permutation test for dbrda under reduced model
## Permutation: free
## Number of permutations: 999
##
## Model: dbrda(formula = its2Dist ~ Depth + Current + Condition(PC1, PC2, PC3), data = its2Model)
## Df SumOfSqs F Pr(>F)
## Model 2 10.084 13.607 0.001 ***
## Residual 215 79.671
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1set.seed(003)
anova(itsDbrdaDepth)## Permutation test for dbrda under reduced model
## Permutation: free
## Number of permutations: 999
##
## Model: dbrda(formula = its2Dist ~ Depth, data = its2Model)
## Df SumOfSqs F Pr(>F)
## Model 1 7.312 18.821 0.001 ***
## Residual 217 84.302
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1set.seed(002)
anova(itsDbrdaCur)## Permutation test for dbrda under reduced model
## Permutation: free
## Number of permutations: 999
##
## Model: dbrda(formula = its2Dist ~ Current, data = its2Model)
## Df SumOfSqs F Pr(>F)
## Model 1 3.362 8.2665 0.001 ***
## Residual 217 88.251
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Prepare data and plot dbRDA
itsRdaVar = round(its2Dbrda$CA$eig/sum(its2Dbrda$CA$eig)*100, 1)
head(itsRdaVar)## MDS1 MDS2 MDS3 MDS4 MDS5 MDS6
## 30.2 18.8 15.3 8.7 8.3 6.9itsRdaVarFit = round(its2Dbrda$CCA$eig/sum(its2Dbrda$CCA$eig)*100, 1)
head(itsRdaVarFit)## dbRDA1 dbRDA2
## 76.1 23.9sintI2P = read.csv("../data/stephanocoeniaMetaData.csv")[-c(66,68,164,166,133,209,211),] %>% dplyr::select("sample" = tubeID, "pop" = site, "depth" = depthZone)
sintI2P$popdepth = paste(sintI2P$pop, sintI2P$depth)
its2RdaPoints = as.data.frame(scores(its2Dbrda, choices=c(1:2))$sites)
its2RdaPoints$sample = sintI2P$sample
head(its2RdaPoints)## dbRDA1 dbRDA2 sample
## 1 -1.43751668 2.8149088 SFK001
## 2 -0.03023976 -0.7189597 SFK002
## 3 -0.03684632 -0.7202105 SFK003
## 4 -1.30458571 2.6305362 SFK004
## 5 -0.66413063 -2.7178081 SFK005
## 6 -0.33555196 2.0829687 SFK006its2DbrdaData1 = sintI2P %>% left_join(its2RdaPoints)## Joining with `by = join_by(sample)`head(its2DbrdaData1)## sample pop depth popdepth dbRDA1 dbRDA2
## 1 SFK001 Tortugas Bank Mesophotic Tortugas Bank Mesophotic -1.43751668 2.8149088
## 2 SFK002 Tortugas Bank Mesophotic Tortugas Bank Mesophotic -0.03023976 -0.7189597
## 3 SFK003 Tortugas Bank Mesophotic Tortugas Bank Mesophotic -0.03684632 -0.7202105
## 4 SFK004 Tortugas Bank Mesophotic Tortugas Bank Mesophotic -1.30458571 2.6305362
## 5 SFK005 Tortugas Bank Mesophotic Tortugas Bank Mesophotic -0.66413063 -2.7178081
## 6 SFK006 Tortugas Bank Mesophotic Tortugas Bank Mesophotic -0.33555196 2.0829687tail(its2DbrdaData1)## sample pop depth popdepth dbRDA1 dbRDA2
## 214 SFK215 Upper Keys Mesophotic Upper Keys Mesophotic -1.77269134 1.2599950
## 215 SFK216 Upper Keys Mesophotic Upper Keys Mesophotic -1.43648298 2.8197042
## 216 SFK217 Upper Keys Shallow Upper Keys Shallow -0.66094456 -2.7030275
## 217 SFK218 Upper Keys Shallow Upper Keys Shallow 0.88196501 0.8116747
## 218 SFK219 Upper Keys Shallow Upper Keys Shallow -0.66633323 -2.7280262
## 219 SFK220 Upper Keys Shallow Upper Keys Shallow -0.03195399 -0.6975144its2EnvLoad = as.data.frame(its2Dbrda$CCA$biplot)
its2EnvLoad$var = row.names(its2EnvLoad)
its2EnvLoad$var = its2EnvLoad$var %>% gsub(pattern = "`", replacement = "", x = its2EnvLoad$var)
its2DbrdaData = merge(its2DbrdaData1, aggregate(cbind(mean.x = dbRDA1, mean.y2 = dbRDA2)~popdepth, its2DbrdaData1, mean), by="popdepth") %>% left_join(gssProf %>% dplyr::select(sample, otu, count) %>% group_by(sample) %>% summarise(count = max(count)) %>% left_join(gssProf %>% dplyr::select(sample, otu, count)) %>% rename(sym = otu) %>% dplyr::select(-count))## Joining with `by = join_by(sample, count)`
## Joining with `by = join_by(sample)`its2DbrdaData$depth = factor(its2DbrdaData$depth)
its2DbrdaData$depth = factor(its2DbrdaData$depth, levels(its2DbrdaData$depth)[c(2,1)])
its2DbrdaData$pop = factor(its2DbrdaData$pop)
its2DbrdaData$pop = factor(its2DbrdaData$pop, levels(its2DbrdaData$pop)[c(4, 1, 3, 2)])
head(its2DbrdaData)## popdepth sample pop depth dbRDA1 dbRDA2 mean.x
## 1 Lower Keys Mesophotic SFK101 Lower Keys Mesophotic -0.03008525 -0.6775244 -0.3002103
## 2 Lower Keys Mesophotic SFK102 Lower Keys Mesophotic -0.66157736 -2.7059632 -0.3002103
## 3 Lower Keys Mesophotic SFK103 Lower Keys Mesophotic -1.42878784 2.8554029 -0.3002103
## 4 Lower Keys Mesophotic SFK104 Lower Keys Mesophotic -0.03611070 -0.7167979 -0.3002103
## 5 Lower Keys Mesophotic SFK105 Lower Keys Mesophotic -0.66193637 -2.7045180 -0.3002103
## 6 Lower Keys Mesophotic SFK106 Lower Keys Mesophotic -0.66955381 -2.7429669 -0.3002103
## mean.y2 sym
## 1 -1.477962 C3-C1-C3.10
## 2 -1.477962 C3/C3.10
## 3 -1.477962 C1/C3-C42.2-C1dl-C3gl-C3gm-C3gk
## 4 -1.477962 C3-C1-C3.10
## 5 -1.477962 C3/C3.10
## 6 -1.477962 C3/C3.10its2DbrdaPlotA = ggplot() +
geom_hline(yintercept = 0, color = "gray90", size = 0.5) +
geom_vline(xintercept = 0, color = "gray90", size = 0.5) +
geom_segment(data = its2EnvLoad, aes(x = 0, y = 0, xend = dbRDA1, yend = dbRDA2), color = "#F5065B", arrow = arrow(length = unit(0.15, "cm"), type = "open"), size = 0.65) +
geom_point(data = its2DbrdaData, aes(x = dbRDA1, y = dbRDA2, fill = sym, shape = depth), color = "black", size = 2, alpha = 1, position = position_jitter(seed = 1, width = 0.075, height = 0.075)) +
scale_shape_manual(values = c(21, 23), name = "Depth Zone") +
geom_text(data = its2EnvLoad[1,], aes(x = dbRDA1-0.22, y = dbRDA2, label = var), color = "#F5065B", size = 3, fontface = "bold") +
geom_text(data = its2EnvLoad[2,], aes(x = dbRDA1, y = dbRDA2+0.2, label = var), color = "#F5065B", size = 3, fontface = "bold") +
scale_fill_manual(values = profPal[-c(3,6,7,8,18)], name = expression(paste(italic("ITS2"), " type profile"))) +
labs(title = "Symbiodiniaceae", x = paste0("dbRDA 1 (", itsRdaVarFit[1], "% [",itsRdaVar[1], "%])"), y = paste0("dbRDA 2 (", itsRdaVarFit[2], "% [",itsRdaVar[2], "%])")) +
guides(shape = guide_legend(override.aes = list(size = 3, stroke = 0.5, alpha = NA), order = 2), fill = guide_legend(override.aes = list(shape = 22, size = 4), order = 1))+
theme_bw()
its2DbrdaPlot = its2DbrdaPlotA +
theme(plot.title = element_text(color = "black", size = 12),
axis.title.x = element_text(color = "black", size = 10),
axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
axis.line.x = element_blank(),
axis.title.y = element_text(color = "black", size = 10),
axis.text.y = element_blank(),
axis.ticks.y = element_blank(),
axis.line.y = element_blank(),
legend.spacing = unit(-5, "pt"),
legend.key.size = unit(5, "pt"),
legend.position = "right",
legend.title = element_text(size = 10),
legend.text = element_text(size = 8),
panel.border = element_rect(color = "black", size = 1),
panel.background = element_rect(fill = "white"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank())
# its2DbrdaPlotdbRdaPlots = (sintDbrdaPlot / its2DbrdaPlot) +
plot_annotation(tag_levels = 'A') &
theme(plot.tag = element_text(size = 16))
ggsave("../figures/figure4.png", plot = dbRdaPlots, height = 18, width = 18, units = "cm", dpi = 300)
ggsave("../figures/figure4.svg", plot = dbRdaPlots, height = 18, width = 18, units = "cm", dpi = 300)save.image("fknmsSint.RData")install.packages(“mclust”) library(mclust)
bams = read.csv(“../data/stephanocoeniaMetaData.csv”)[-c(66,68,164,166,209,211),] %>% dplyr::select(“sample” = tubeID, “pop” = site, “depth” = depthZone) # list of bams files and their populations (tech reps removed)
ma = as.matrix(read.table(“../data/snps/sintFiltSnps.ibsMat”)) # reads in IBS matrix produced by ANGSD # ma = as.matrix(read.table(“../data/snps/sintNoClones.ibsMat”)) # reads in IBS matrix produced by ANGSD
dimnames(ma) = list(bams[,1],bams[,1])
d_clust <- Mclust(ma, G=1:15, modelNames = mclust.options(“emModelNames”))
genad2 = cbind(gen_ad,d_clust$classification)